{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('y_train: ', (49000,))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "    print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76984946819e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.39910036865e-11\n",
      "dw error:  9.9042118654e-11\n",
      "db error:  2.41228675681e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 5e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.27563491363e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 3e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  2.29957917731e-11\n",
      "dw error:  8.16201110576e-11\n",
      "db error:  7.82672402146e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print('Testing affine_relu_forward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.9996027491\n",
      "dx error:  1.40215660067e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.3025458445\n",
      "dx error:  9.38467316199e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.83e-08\n",
      "W2 relative error: 3.12e-10\n",
      "b1 relative error: 9.83e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 7.98e-08\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "    print('Running numeric gradient check with reg = ', reg)\n",
    "    model.reg = reg\n",
    "    loss, grads = model.loss(X, y)\n",
    "\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "        print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 2070) loss: 2.305961\n",
      "(Epoch 0 / 10) train acc: 0.113000; val_acc: 0.084000\n",
      "(Iteration 101 / 2070) loss: 1.782752\n",
      "(Iteration 201 / 2070) loss: 1.660997\n",
      "(Epoch 1 / 10) train acc: 0.411000; val_acc: 0.413000\n",
      "(Iteration 301 / 2070) loss: 1.623456\n",
      "(Iteration 401 / 2070) loss: 1.580220\n",
      "(Epoch 2 / 10) train acc: 0.485000; val_acc: 0.455000\n",
      "(Iteration 501 / 2070) loss: 1.516204\n",
      "(Iteration 601 / 2070) loss: 1.451538\n",
      "(Epoch 3 / 10) train acc: 0.488000; val_acc: 0.468000\n",
      "(Iteration 701 / 2070) loss: 1.622765\n",
      "(Iteration 801 / 2070) loss: 1.488118\n",
      "(Epoch 4 / 10) train acc: 0.514000; val_acc: 0.491000\n",
      "(Iteration 901 / 2070) loss: 1.405215\n",
      "(Iteration 1001 / 2070) loss: 1.423761\n",
      "(Epoch 5 / 10) train acc: 0.505000; val_acc: 0.484000\n",
      "(Iteration 1101 / 2070) loss: 1.345751\n",
      "(Iteration 1201 / 2070) loss: 1.259718\n",
      "(Epoch 6 / 10) train acc: 0.585000; val_acc: 0.499000\n",
      "(Iteration 1301 / 2070) loss: 1.333484\n",
      "(Iteration 1401 / 2070) loss: 1.344973\n",
      "(Epoch 7 / 10) train acc: 0.581000; val_acc: 0.500000\n",
      "(Iteration 1501 / 2070) loss: 1.315636\n",
      "(Iteration 1601 / 2070) loss: 1.407963\n",
      "(Epoch 8 / 10) train acc: 0.568000; val_acc: 0.498000\n",
      "(Iteration 1701 / 2070) loss: 1.173208\n",
      "(Iteration 1801 / 2070) loss: 1.253118\n",
      "(Epoch 9 / 10) train acc: 0.582000; val_acc: 0.507000\n",
      "(Iteration 1901 / 2070) loss: 1.245580\n",
      "(Iteration 2001 / 2070) loss: 1.303172\n",
      "(Epoch 10 / 10) train acc: 0.582000; val_acc: 0.521000\n"
     ]
    }
   ],
   "source": [
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "\n",
    "model = TwoLayerNet()\n",
    "solver = Solver(model, data,\n",
    "                  update_rule='sgd',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  lr_decay=0.95,\n",
    "                  num_epochs=10, batch_size=236,\n",
    "                  print_every=100)\n",
    "solver.train()\n",
    "\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GoK0NI0MlTB64B8/s2YaSY7pkIeDHTERERERE6Qws9AYsBiNDJYwMlTA+UcaXxiYxF7k9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\nU/fB2n+8sJa+XTty3dj0h2qvn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1896dbc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.30794143344\n",
      "W1 relative error: 4.18e-07\n",
      "W2 relative error: 6.68e-07\n",
      "W3 relative error: 3.33e-07\n",
      "b1 relative error: 2.57e-08\n",
      "b2 relative error: 7.53e-09\n",
      "b3 relative error: 6.71e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.76512233379\n",
      "W1 relative error: 1.44e-08\n",
      "W2 relative error: 2.36e-08\n",
      "W3 relative error: 4.82e-07\n",
      "b1 relative error: 5.47e-08\n",
      "b2 relative error: 4.61e-09\n",
      "b3 relative error: 1.08e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(251)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "    print('Running check with reg = ', reg)\n",
    "    model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print('Initial loss: ', loss)\n",
    "\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.349788\n",
      "(Epoch 0 / 20) train acc: 0.300000; val_acc: 0.147000\n",
      "(Epoch 1 / 20) train acc: 0.300000; val_acc: 0.147000\n",
      "(Epoch 2 / 20) train acc: 0.420000; val_acc: 0.128000\n",
      "(Epoch 3 / 20) train acc: 0.600000; val_acc: 0.153000\n",
      "(Epoch 4 / 20) train acc: 0.660000; val_acc: 0.137000\n",
      "(Epoch 5 / 20) train acc: 0.700000; val_acc: 0.148000\n",
      "(Iteration 11 / 40) loss: 1.034961\n",
      "(Epoch 6 / 20) train acc: 0.680000; val_acc: 0.174000\n",
      "(Epoch 7 / 20) train acc: 0.860000; val_acc: 0.191000\n",
      "(Epoch 8 / 20) train acc: 0.920000; val_acc: 0.165000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.202000\n",
      "(Epoch 10 / 20) train acc: 0.940000; val_acc: 0.178000\n",
      "(Iteration 21 / 40) loss: 0.273254\n",
      "(Epoch 11 / 20) train acc: 0.920000; val_acc: 0.152000\n",
      "(Epoch 12 / 20) train acc: 0.980000; val_acc: 0.168000\n",
      "(Epoch 13 / 20) train acc: 0.960000; val_acc: 0.173000\n",
      "(Epoch 14 / 20) train acc: 0.960000; val_acc: 0.188000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.181000\n",
      "(Iteration 31 / 40) loss: 0.084068\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.192000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.184000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.180000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.177000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.186000\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1896db950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2\n",
    "learning_rate = 1e-2\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 4.542533\n",
      "(Epoch 0 / 20) train acc: 0.180000; val_acc: 0.082000\n",
      "(Epoch 1 / 20) train acc: 0.340000; val_acc: 0.155000\n",
      "(Epoch 2 / 20) train acc: 0.640000; val_acc: 0.123000\n",
      "(Epoch 3 / 20) train acc: 0.780000; val_acc: 0.154000\n",
      "(Epoch 4 / 20) train acc: 0.900000; val_acc: 0.148000\n",
      "(Epoch 5 / 20) train acc: 0.920000; val_acc: 0.160000\n",
      "(Iteration 11 / 40) loss: 0.400350\n",
      "(Epoch 6 / 20) train acc: 0.940000; val_acc: 0.154000\n",
      "(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.155000\n",
      "(Epoch 8 / 20) train acc: 0.940000; val_acc: 0.163000\n",
      "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.153000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.144000\n",
      "(Iteration 21 / 40) loss: 0.191309\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.162000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.147000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.162000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.155000\n",
      "(Iteration 31 / 40) loss: 0.038223\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.156000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.155000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.151000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.158000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.161000\n"
     ]
    },
    {
     "data": {
      "image/png": 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bAAAdzWPYunHSBUwB58A5SJyDxDlInIPEOUicg6TjOZi7MVsAAOM0j3e2AADG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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1449aff90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 1e-2\n",
    "weight_scale = 5e-2\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "Not really, they both seem to run fast. It was harder to tune them I guess.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.773038\n",
      "(Epoch 0 / 5) train acc: 0.150000; val_acc: 0.119000\n",
      "(Iteration 11 / 200) loss: 2.173145\n",
      "(Iteration 21 / 200) loss: 2.085849\n",
      "(Iteration 31 / 200) loss: 2.001671\n",
      "(Epoch 1 / 5) train acc: 0.255000; val_acc: 0.247000\n",
      "(Iteration 41 / 200) loss: 2.079665\n",
      "(Iteration 51 / 200) loss: 2.024380\n",
      "(Iteration 61 / 200) loss: 1.937272\n",
      "(Iteration 71 / 200) loss: 1.882731\n",
      "(Epoch 2 / 5) train acc: 0.331000; val_acc: 0.274000\n",
      "(Iteration 81 / 200) loss: 1.822196\n",
      "(Iteration 91 / 200) loss: 1.943827\n",
      "(Iteration 101 / 200) loss: 1.665939\n",
      "(Iteration 111 / 200) loss: 2.100088\n",
      "(Epoch 3 / 5) train acc: 0.357000; val_acc: 0.298000\n",
      "(Iteration 121 / 200) loss: 1.962170\n",
      "(Iteration 131 / 200) loss: 1.883785\n",
      "(Iteration 141 / 200) loss: 1.750005\n",
      "(Iteration 151 / 200) loss: 1.746819\n",
      "(Epoch 4 / 5) train acc: 0.418000; val_acc: 0.323000\n",
      "(Iteration 161 / 200) loss: 1.712724\n",
      "(Iteration 171 / 200) loss: 1.681163\n",
      "(Iteration 181 / 200) loss: 1.751546\n",
      "(Iteration 191 / 200) loss: 1.508748\n",
      "(Epoch 5 / 5) train acc: 0.438000; val_acc: 0.344000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.872107\n",
      "(Epoch 0 / 5) train acc: 0.122000; val_acc: 0.125000\n",
      "(Iteration 11 / 200) loss: 2.219653\n",
      "(Iteration 21 / 200) loss: 2.100984\n",
      "(Iteration 31 / 200) loss: 2.051777\n",
      "(Epoch 1 / 5) train acc: 0.312000; val_acc: 0.296000\n",
      "(Iteration 41 / 200) loss: 1.876294\n",
      "(Iteration 51 / 200) loss: 1.833868\n",
      "(Iteration 61 / 200) loss: 1.659827\n",
      "(Iteration 71 / 200) loss: 1.793510\n",
      "(Epoch 2 / 5) train acc: 0.385000; val_acc: 0.318000\n",
      "(Iteration 81 / 200) loss: 1.778328\n",
      "(Iteration 91 / 200) loss: 1.782361\n",
      "(Iteration 101 / 200) loss: 1.672057\n",
      "(Iteration 111 / 200) loss: 1.574062\n",
      "(Epoch 3 / 5) train acc: 0.450000; val_acc: 0.360000\n",
      "(Iteration 121 / 200) loss: 1.656812\n",
      "(Iteration 131 / 200) loss: 1.545180\n",
      "(Iteration 141 / 200) loss: 1.630373\n",
      "(Iteration 151 / 200) loss: 1.441170\n",
      "(Epoch 4 / 5) train acc: 0.448000; val_acc: 0.322000\n",
      "(Iteration 161 / 200) loss: 1.503656\n",
      "(Iteration 171 / 200) loss: 1.558157\n",
      "(Iteration 181 / 200) loss: 1.199709\n",
      "(Iteration 191 / 200) loss: 1.629145\n",
      "(Epoch 5 / 5) train acc: 0.527000; val_acc: 0.361000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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U+qHDdkMAfA7AM4bbjs8WUoGIHA/gIgDb/TaaiIiIiIiIvFW5nArgKwC2iUhuJdQ7AIwC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\nWX3jkgc6nZH62jRaOqahpWt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x81dbbuPlPNANxIP8HPo5f90eG/QFBnOwcvmkBvz6rf3R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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1449756d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "    print('running with ', update_rule)\n",
    "    model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "    solvers[update_rule] = solver\n",
    "    solver.train()\n",
    "    print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "    plt.subplot(3, 1, 1)\n",
    "    plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "\n",
    "    plt.subplot(3, 1, 2)\n",
    "    plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "    plt.subplot(3, 1, 3)\n",
    "    plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "\n",
    "for i in [1, 2, 3]:\n",
    "    plt.subplot(3, 1, i)\n",
    "    plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.13956917985e-07\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 2.529380\n",
      "(Epoch 0 / 5) train acc: 0.139000; val_acc: 0.127000\n",
      "(Iteration 11 / 200) loss: 2.087800\n",
      "(Iteration 21 / 200) loss: 1.801532\n",
      "(Iteration 31 / 200) loss: 1.780462\n",
      "(Epoch 1 / 5) train acc: 0.394000; val_acc: 0.331000\n",
      "(Iteration 41 / 200) loss: 1.605401\n",
      "(Iteration 51 / 200) loss: 1.652368\n",
      "(Iteration 61 / 200) loss: 1.689570\n",
      "(Iteration 71 / 200) loss: 1.706472\n",
      "(Epoch 2 / 5) train acc: 0.441000; val_acc: 0.347000\n",
      "(Iteration 81 / 200) loss: 1.665940\n",
      "(Iteration 91 / 200) loss: 1.519702\n",
      "(Iteration 101 / 200) loss: 1.630879\n",
      "(Iteration 111 / 200) loss: 1.387827\n",
      "(Epoch 3 / 5) train acc: 0.502000; val_acc: 0.353000\n",
      "(Iteration 121 / 200) loss: 1.501658\n",
      "(Iteration 131 / 200) loss: 1.383606\n",
      "(Iteration 141 / 200) loss: 1.278994\n",
      "(Iteration 151 / 200) loss: 1.386835\n",
      "(Epoch 4 / 5) train acc: 0.505000; val_acc: 0.374000\n",
      "(Iteration 161 / 200) loss: 1.175158\n",
      "(Iteration 171 / 200) loss: 1.425357\n",
      "(Iteration 181 / 200) loss: 1.328646\n",
      "(Iteration 191 / 200) loss: 1.145576\n",
      "(Epoch 5 / 5) train acc: 0.542000; val_acc: 0.357000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.803023\n",
      "(Epoch 0 / 5) train acc: 0.108000; val_acc: 0.121000\n",
      "(Iteration 11 / 200) loss: 2.126556\n",
      "(Iteration 21 / 200) loss: 2.015316\n",
      "(Iteration 31 / 200) loss: 2.095618\n",
      "(Epoch 1 / 5) train acc: 0.354000; val_acc: 0.313000\n",
      "(Iteration 41 / 200) loss: 1.947670\n",
      "(Iteration 51 / 200) loss: 1.738789\n",
      "(Iteration 61 / 200) loss: 1.712757\n",
      "(Iteration 71 / 200) loss: 1.699025\n",
      "(Epoch 2 / 5) train acc: 0.461000; val_acc: 0.335000\n",
      "(Iteration 81 / 200) loss: 1.539297\n",
      "(Iteration 91 / 200) loss: 1.520616\n",
      "(Iteration 101 / 200) loss: 1.516899\n",
      "(Iteration 111 / 200) loss: 1.425227\n",
      "(Epoch 3 / 5) train acc: 0.487000; val_acc: 0.348000\n",
      "(Iteration 121 / 200) loss: 1.616402\n",
      "(Iteration 131 / 200) loss: 1.437630\n",
      "(Iteration 141 / 200) loss: 1.554402\n",
      "(Iteration 151 / 200) loss: 1.526326\n",
      "(Epoch 4 / 5) train acc: 0.491000; val_acc: 0.339000\n",
      "(Iteration 161 / 200) loss: 1.468514\n",
      "(Iteration 171 / 200) loss: 1.426716\n",
      "(Iteration 181 / 200) loss: 1.351280\n",
      "(Iteration 191 / 200) loss: 1.377368\n",
      "(Epoch 5 / 5) train acc: 0.555000; val_acc: 0.352000\n",
      "\n"
     ]
    },
    {
     "data": {
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YtGkT06dP9wcLv/nNbyguLubCCy+kffv2nHnmmf51H3jgAa6++moGDhzIOeecw0knndTg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uBI6ViMMnjD0YzgrjtRB1gL6gyC4HnunbjUuwaeLmkxV05aUO1eA4/7HADOl+ZxKWqqM0zRw9L1\nufxxYL2Op2EWWWGWQiMTi4E+t9KsWSm5Ttmkqd2KzljIVTDjSYojHVI2J1/qiGa9qHqBVX3V/Xhx\n5mxU+56AH5L8v7QxEtKocZZUtMJFUvPZuwa3twFmpcqmGU+d357G5BBL2UD53Gf1cJqWnRqUz8or\nB/a/+2suiw9Lvot9BJf5SwQCgUAgEDCpmC+pSAZHACDSnxzqo0ZS5BqmL5cW9XJkQdjG3Q24pu4U\nTO58GKcd/xMmdz6Ma+pOwcbdDY4fNw+lIT82JytRGfsNTjv+J1TGfoNbSv8T189fjTGffILRu/6B\n4KIHM8dSPNkL4vMqPkOewctIwltBLxAJlkseb13KxZynq1NaODpMOnBvSFlApBfAzGsDGJe4GZ0T\nJ8VUK+ahIUeBvinpoDZyAADtLi2tX4fg7Nko+cUKeEpLAULgKS3tVuIEdMeikQ7R/Trd+w7dNhqn\nLt2CKdXbse2Rp7F/2nTsGzMW+6dNR6SmxtIpzRtfhvsvPxtlIT8IJDVGuaKlXmA1uP0INiUrcVvs\nejTQIlAQBM8pQsnNV2TGwBWgKJnUqhDPCZAYlnjXZY5bb77pBTyZxxm/PR3Uh5WJ7t8LvfFTz32W\nIA9g0LvKmgfrbwCqgpI6Z/26zEuLb10EUlioeLuRr6ppINuHELYFAoFAIBDkEfluNYUk5a5WrqxJ\nVkpWBGoMdqp7q4S3kXWAXjYk09fS2NAt0y9bqOpJwmswsnJYfwP0JPhRZeGzOeC+Ngb2ALj8ccds\nTL6s+hpToTEBFzygPauoa0dKn3HdO6gPS+MLsDlZqXm5/B50hUYoznnj7gZFae7nn2sN0BXWDjbY\nP226qfUDwJ43tCoEwpgzSUpw2vE/AdDed+nfoq9/WIuFe15Egaz/THNOst+eg8kh2JY8F9Nde0wt\nFPTmvhrde0FvHqRRzX2enji98faUlmL09m2mx5wPhG2BQCAQCAS9DHVwM8dVqzBbTpdJDfb6ADAC\nOoMStV6l2igjG4XMdOmeXsDBkoRnwujTivjmIrzoMSQaS+AJJDTBYi76CLmvjVF5rYM2JnoKjR9N\nuMd6n2KusCM4pBqjtLfh5uQFmpeq70G1yMyeLY9jBXk883zXh1AEc4BzZXqsUtJOtxdrx14CICXJ\nv3crhkZbsf/VUkWwQvTKImVZSbkth/y3qGHEBFAAP9z3VwyNtsLLCoRkvz1r7rkLS+KrDS0UDi77\nL+n7W82LAWc07sZPal/Dvqd+og3CzK63qn9UXfa7cXcDVlVvZ/72mJbu9iFEQCcQCAQCQZ5Ql1gu\n8azrXkimkMqkngdwN9dn25H/zjXZKmTySMLrIluIakQQOjxoqgsCgBTU5ai8kPvamPU4OmRjwqPQ\nmHfsCvfIxigA4LubH8PP31+IYtqML0kRViW+iw2JKcx7UB4kLIg9g4Cr+3ld2w0HyvTUwjItgRCe\nOPNb2DFigrkkP2POHKduBEinQiSlplXKUKp/i3aMmIAdIyZYyuhbsVBwxY7jiwd+idJL72LOfTch\nSFKKyw59iOv3vAhX7Dj7vPTmgRydoM+sRzk4ezbe+/wIfGsfxeD2IzjcbxBi192EM/tY/xwgAjqB\nQCAQCPKGOiNTSlqYr1NLlFvBlvl3L8XpgIMpgtDlQrh+AILnFOWsvJD72jiYhTPDqkJjXlAre7p9\nQJcs2Mo24K5fh0kf3gUgChCgFC1YVfAEgoVelMbZ92A6SCh1HVI8rCvaM2Sg9hyyuG7yDNPG3Q34\n+/oPgXiXuSS/as60oj8CiGIwOQZAm/23k9G3aqHgaQnrzv106ef+ab9GIhXMMc9Lz7ZDjk6Qb9Sj\nnMlShosRvXhZ97GF3bh/d0NOxXFygQjoBAKBQCDIE+pMTSMtQjkrqMui7M+W+XcvxsmAQ1cEIeo1\n78uyQVbXxqEsXK5xzMeLpezp8gL+wZJdg52gVsfGoCr4EoARhpnATpWPor7tRpslA26e8ZLPm6FW\nJPllc8b3wJnwRY8qXivP/vNmjeW9v38rLFKUQusFuWF/yHTumwqTKALVAwAIFP2lBkG+WdBqFvD1\nJURAJxAIBAJBnlDvVq9MzMcD3jVK6XgbZX/ZljaeLJj6l9nBJDPTZ6+NwXk56uPFUvZMxgFfP+C2\nz+ydg1E/np7ITOoeDFyyAolNt2QUUdP9luH6AUh0uLtFe4o7TQ24sxmv9LzZ/2op19zVy/KnH188\n8ww0PrQU0z6sA+0ASADYfvYklH63GoDW/Ls9lsiYf98X+47id4sd5FJ8et4ZmArjuW/pnpRvbnBk\nQM2C1t7ad5wNwrZAIBAIBII8oZYt3zXwYnw04Z682AcI+GXNLWMgsd+nMTkvIx8vbuyIoJhhZPVg\nZuFRMR+euY90P0/cCI6KYvScMMZc1YTRc8JSkBcsNz0HO+PFPXdN7C0uens1pr1XB9pBABDQDoJp\n79XhwK9uw7l3v4rFL36QsRpojcYzwRwAbE5W4rb4AnyJoQAIBpx6HCWTIvAEEgAoPIEESia14qqv\nvyO9oX6dpFZZFbJtNYCK+VI2vapV+tPgt3LxzDPg9yrLQeWlznrZyN7Qd8yLsC0QCAQCgUBw0uBY\niaAcOxL7vRmT89o3ZizAWkcSgjH79jr6XbZg2BhEDgxE+B9lSBxq45sHRlYYmbJA9jnYHS+uuWt0\nnBXzsX/iGCSOMQ4lQPGtGb8yPRZAKn78rHqWbZuNnNyTKeSZRnW5ZzaWKvlG2BYIBAKBQCAQqFDL\nmjtCNtkljtIxo0VpTjE5L0dLWM2UPVWox+Shsfsx6Z+PsMdTJRgSCZeiqc4DGosA4CwVNROsMTgH\nK+NldK255q7JcSaOUUghmRLaYe3jAVkmy0iR1KQMFTA/LzsBn1G554nUdywCOoFAIBAIBAI78Ers\nWxDPSGMmvZ5+TU4WpSbn5aiPF4eyp3pMJrS9hnG71gA6XnKZP1P/H542HTSmDKy4vOT0BGtMzsFs\nvKxcay4MhHU8/YlOhs7aRytUWo2C8fU3sD/AYimtad+hamOk7vRbsGjvaMv3Qp/tbVUheugEAoFA\nIBAI7DB9ubSAlWMkbmOUtVBhpMQHdAcB6X6ndBCwcXdDtmfTjcl5BWfPRskvVsBTWgoQAk9pKUp+\nsSL7DKjF/iiWn6Nfz0uOgamyoh0MzsFsvMyutZMU//ByELeyTJK4KV4dy67u87oIBgW8IADKQn5l\nWaJRH6JJL58Zhn2HjB7PcbvuxIS215y/F3o5phk6QsgfAVwKIEwpHcd4/nsAboOUtz0K4EeU0g9S\nz32eeqwLQMJKDahAIBAIBAJBn4LXN46jRLNHpdctnFdOSlhNsOrnqDfOOVU7NcFovPKpuhj88b0A\ngPCT65E4RkECwKtjJ+LX5f8OQArg+hd60NoRt2ezwVlKq8Yw+GZsjPhJDEs867A5Jhmo91UbAl6s\nlFyuBfBbAE/rPP8ZgAsppUcIIZcAeBzAN2TPX0Qp1bnTBAKBQCAQ5A2bpsc5+6wTAR7fOI4STSvS\n63NctVjiWYdS0oJGWoSVifmoaa3kOnxdOM4rX71+dv0cHS0VNcNBmX2nCf743kxgt3F3A55/5R8g\nTl873s0OFYbBd2QX8z2lRGkG3xdtCHgxLbmklL4J4LDB829TSo+k/voOAH43VIFAIBAIBLnFSWn9\nE1WmP19wlGiaSa9f2/9dVHvXoNzVAhcByl0tqPauwbX9383Z4bNglX7WbliNjgfOZMrV20E9JisT\n8xGlPuWLDLJAjpeK6sF5n5hd61wyb3wZdi6dhs+qZ2Hn0mn2gjm1TQFg2WpAjaGtgU7A3kiHKP4u\nD4g37m7AlOrtOHXpFkyp3n7ClGM63UN3PYCtsr9TAK8SQnYRQnS6IiUIITcQQt4jhLzX3Nxs9FKB\nQCAQCAS8cPRt5fWzTkbMvM9kqL0L1f1LS7zPI6DqHwuQGJZ4n+9+wMAHzCnUpZ9zXLVYQR5HINoE\np4N+J/wcg7NnY/T2bRizby9Gb9+Wm7JRzvuEda2fnvQF5u2YmdNrxyJSU4P906Zj35ix2D9tOiI1\nNdbe6MBmj/y7ww8+hOBl89jBN2NjJEp9WJnovu7ygDin/aY9jCUfOkLIKAB/YfXQyV5zEYDVACop\npYdSj5VRShsIIcUAXgNwSyrjZ4jwoRMIBAKBwGGMvKKqWnvuswT2MLsWJn5kTnHq0i2Ko6j1/RTl\nLlYZZB/35uPB7n2Sp2unRq0sCUhZMUtZTJt+gtzfzaFyOaV6O7OktSzkx86l00yPrSfIqw8dIaQC\nwBoAl6SDOQCglDak/gwTQjYAOB+AaUAnEAgEAoHAYXil9fP1Wb2JvtgXaHYtLPiAOYG6/4tXqOSE\nxO59YnbteOerxdcbKUuaBnTZeDLa+W5Vj+ckADvnsD87n6Iz+cZ2ySUhZCSA9QB+QCn9H9nj/Qgh\nA9L/D2AGgJNkS0YgEAgEgl4Gr7S+GnnZXqwdcFvvWeoT9NW+QLPranOBbZXFM8/Alb63Uev7KT4t\nuBpJvSVmXw/6ebB7zxldOwvzVVE2WTkZkYd/Zml+W7F10C3JtGlTkEtLCT1xmVyJzuQT04COEPJn\nAH8DcAYh5CAh5HpCyE2EkJtSL1kOYAiA1YSQPYSQdK3kMAC1hJAPALwLYAul9K85OAeBQCAQCARm\ncPRtaVAvHqOHAUoB/2D+z+qt9NW+QLPranOBbZV57p0KcRYPSWqLDft60M+LnXsOML52JvM1XbqY\naGwEKEWiJYKmdwKIfO5nvl6Onn1D+nHNZ6fMviM1NbaDWLPvVmPa6yfbiHqN3IwrfW8rns6X6Eyu\nsdRDl29ED51AIBAIBL0Im30xfYITtS8wX31YenOEuAGa7DslrL0Jo2u3/gYYzdf906az5f4DCYye\nE9a8Xk6kpgZNy5aBxuLdr/J5UXLvvQjOnq3/2aWlGL19m63SZZ4eOtPXMsYv4S7EPeQmPHXs/Jxa\nazhFXnvoBAKBQCAQnMDkqWyvRzlR+wJt+oBZRm8u0KQzAXFf7G+0i9G127bCcL7qli52KG0RWPM7\neEoUmHQE4d2FSHS44Ql0oXj8Melxo89OP87jyaj+7lTQFn7wISSamuApKUHxrYuY/XOm/XaMLKan\nqxNVwZdQdefdWR1fb0UEdAKBQCAQCIw5UYMdOdOXs7MhJ0KJoI0FtmVyOUfUmZZ0/xfQ94I63sBU\n79qZzFddQ+5AF/P1CratQHBEG4Ij2jSPo2K+sdl3NqjGJDh9OYLbt5m+zTSwPBk2olI47UMnEAgE\nAoHgRMOuuENfwG6/U18iF750uZwjuehvzIM3H/M7nRLeMZmvTENunxfFk73M1yswCYQMzb55sTEm\npv12eeof7Q2IDJ1AIBAIBAJj8lW219PkI5PV0+Qq25XLOeJ0pqWnMn5OW0gYzFee0kXtm42zrbY+\nW42NMSm+dRGzhy4TWFrJup8gpbxCFEUgEAgEAoHgZKEvCtw4fcw9NQZ9RXgn10I68iCKOR6A1TGJ\n1NQYB5bqgG30DGD/q9Lf/YOA2DGgK9b9+jwYt/MgRFEEAoFAIBAIBEr6Yl+R0/2NPTUGfaUXNZfZ\nVlawyMLimARnzzbODMqzmOrvjh7Wvt5OxrQHEQGdQCAQCAQCwclCXwkq5DgdYPTUGPQl4Z1clR+z\nSizV5GpMrHw30Ls3N3QQAZ1AIBAIBALByUJfCirkOBlg9NQYnCy9qEYYBkskt2NiNVDrzZsbOoiA\nTiAQCAQCgeBkQQQVPTsGZoHpCSLSoYtudjQPPZx63y2nL2xuMBCiKAKBQCAQCAQCQU+TazGS3kBP\nniPru11eoGAAED3SKwNoIYoiEAgEAoFAIMgtJ3pGKZ84bWvQG8l1dtRoPp7A2WkR0AkEAoFAIBAI\n+OkpP7cTlb6oQJoNuRJcsTIfT1CvSVdPH4BAIBAIBAKBoA9ilFES8KMnxtEHRTp6hJN4PoqATiAQ\nCAQCgUDAz8mSUcoX05dL/WRy+qhIR49wEs9HEdAJBAKBQCAQCPgRGSVnqZgvicEMWW4AACAASURB\nVIMER0CS8B9xYgmi5JqTeD6KHjqBQCAQCAQCAT991dOuN3OC9njlhZN4PooMnUAgEAgEAoGAH5FR\nEvQmTuL5KHzoBAKBQCAQCAQCgaCXYdWHTmToBAKBQCAQCAQCgaCPIgI6gUAgEAgEAoFAIOijiIBO\nIBAIBAKBQCAQCPooIqATCAQCgUAgEAgEgj6KCOgEAoFAIBAIBAKBoI8iAjqBQCAQCAQCgUAg6KP0\nStsCQkgzgC96+jgYFAFo6emDOEkRY9+ziPHvOcTY9yxi/HsWMf49hxj7nkWMf8/Rm8b+FErpULMX\n9cqArrdCCHnPiheEwHnE2P9/9u47PK7qTPz498xo1KVRtdVsy3KRZUtyl2yKY3CCDdiUhBBMaOHH\nJtmEmoQEkgAOu5tN2SyQZJ/dhISEsJCFBEKQTQskhNAsbGNLtmUbW25qLpI16pp2fn/cO0XVstqo\nvJ/n8aOZe86998zVeDTvPee8J7Tk+oeOXPvQkusfWnL9Q0eufWjJ9Q+d8XjtZcilEEIIIYQQQoxT\nEtAJIYQQQgghxDglAd25+WWoGzCJybUPLbn+oSPXPrTk+oeWXP/QkWsfWnL9Q2fcXXuZQyeEEEII\nIYQQ45T00AkhhBBCCCHEOCUBnRBCCCGEEEKMUxLQDYBSap1Sar9S6qBS6r5Qt2eiU0pNU0r9TSm1\nVym1Ryl1l7l9k1KqWim10/x3WajbOhEppY4opcrNa7zN3JaklPqLUupj82diqNs5ESmlcoPe3zuV\nUk1KqbvlvT9ylFJPKKVOKqV2B23r9f2uDD81/xaUKaWWhK7l418f1/7HSql95vX9k1IqwdyerZRq\nD/o/8D+ha/nE0Mf17/OzRil1v/ne36+UWhuaVk8MfVz7Z4Ou+xGl1E5zu7z3h1k/3zPH7We/zKE7\nC6WUFTgAfAqoAj4ENmqt94a0YROYUiodSNda71BKxQHbgauAa4EWrfV/hLSBE5xS6giwTGt9Omjb\nj4AGrfUPzJsaiVrrb4WqjZOB+dlTDRQDX0De+yNCKbUKaAF+p7XON7f1+n43v9zeAVyG8Xt5TGtd\nHKq2j3d9XPtLgL9qrd1KqR8CmNc+G9jsqyeGro/rv4lePmuUUvOB3wNFQAbwBjBXa+0Z1UZPEL1d\n+27lPwEcWuuH5b0//Pr5nnkL4/SzX3rozq4IOKi1rtRaO4H/A64McZsmNK11rdZ6h/m4GagAMkPb\nqknvSuBJ8/GTGB98YmStAQ5prY+GuiETmdb6baCh2+a+3u9XYnwB01rrD4AE84uBGITerr3W+nWt\ntdt8+gGQNeoNmyT6eO/35Urg/7TWnVrrw8BBjO9HYhD6u/ZKKYVxA/v3o9qoSaSf75nj9rNfArqz\nywSOBz2vQoKLUWPemVoMbDU33W52dz8hw/5GjAZeV0ptV0p90dw2VWtdaz6uA6aGpmmTynV0/YMu\n7/3R09f7Xf4ejK5bgVeCns9USn2klPq7UurCUDVqEujts0be+6PnQuCE1vrjoG3y3h8h3b5njtvP\nfgnoxJillIoFngfu1lo3Af8NzAIWAbXAT0LYvInsAq31EuBS4Kvm0BA/bYzTlrHaI0gpFQ5cAfzB\n3CTv/RCR93toKKW+A7iBp81NtcB0rfVi4GvAM0qp+FC1bwKTz5rQ20jXm3ny3h8hvXzP9Btvn/0S\n0J1dNTAt6HmWuU2MIKWUDeM/2dNa6xcAtNYntNYerbUXeBwZ7jEitNbV5s+TwJ8wrvMJ3/AC8+fJ\n0LVwUrgU2KG1PgHy3g+Bvt7v8vdgFCilbgHWA583v1RhDvWrNx9vBw4Bc0PWyAmqn88aee+PAqVU\nGPBp4FnfNnnvj4zevmcyjj/7JaA7uw+BOUqpmeZd8+uAl0LcpgnNHD/+a6BCa/2fQduDxytfDezu\nvq8YGqVUjDlBGKVUDHAJxnV+CbjZrHYz8OfQtHDS6HKHVt77o66v9/tLwE1mxrMVGEkLans7gBgc\npdQ64JvAFVrrtqDtqWaiIJRSOcAcoDI0rZy4+vmseQm4TikVoZSaiXH9S0e7fZPAJ4F9Wusq3wZ5\n7w+/vr5nMo4/+8NC3YCxzsy0dTvwGmAFntBa7wlxsya684EbgXJf2l7g28BGpdQijC7wI8CXQtO8\nCW0q8Cfjs44w4Bmt9atKqQ+B55RS/w84ijFhW4wAM5D+FF3f3z+S9/7IUEr9HlgNpCilqoCHgB/Q\n+/v9ZYwsZweBNozso2KQ+rj29wMRwF/Mz6EPtNZfBlYBDyulXIAX+LLWeqAJPUQv+rj+q3v7rNFa\n71FKPQfsxRgK+1XJcDl4vV17rfWv6Tl3GuS9PxL6+p45bj/7ZdkCIYQQQgghhBinZMilEEIIIYQQ\nQoxTEtAJIYQQQgghxDglAZ0QQgghhBBCjFMS0AkhhBBCCCHEOCUBnRBCCCGEEEKMUxLQCSGEGPeU\nUi3mz2yl1PXDfOxvd3v+3nAeXwghhBgKCeiEEEJMJNnAOQV0SqmzrcnaJaDTWp93jm0SQgghRowE\ndEIIISaSHwAXKqV2KqXuUUpZlVI/Vkp9qJQqU0p9CUAptVop9Q+l1EsYiyWjlHpRKbVdKbVHKfVF\nc9sPgCjzeE+b23y9gco89m6lVLlS6nNBx35LKfVHpdQ+pdTTylwlWwghhBhuZ7srKYQQQown9wHf\n0FqvBzADM4fWerlSKgJ4Vyn1ull3CZCvtT5sPr9Va92glIoCPlRKPa+1vk8pdbvWelEv5/o0sAhY\nCKSY+7xtli0GFgA1wLvA+cA7w/9yhRBCTHbSQyeEEGIiuwS4SSm1E9gKJANzzLLSoGAO4E6l1C7g\nA2BaUL2+XAD8Xmvt0VqfAP4OLA86dpXW2gvsxBgKKoQQQgw76aETQggxkSngDq31a102KrUaaO32\n/JPASq11m1LqLSByCOftDHrsQf7eCiGEGCHSQyeEEGIiaQbigp6/BvyzUsoGoJSaq5SK6WU/O3DG\nDObmASuCyly+/bv5B/A5c55eKrAKKB2WVyGEEEIMkNwxFEIIMZGUAR5z6ORvgccwhjvuMBOTnAKu\n6mW/V4EvK6UqgP0Ywy59fgmUKaV2aK0/H7T9T8BKYBeggW9qrevMgFAIIYQYFUprHeo2CCGEEEII\nIYQYBBlyKYQQQgghhBDjlAR0QgghhBBCCDFOSUAnhBBizDATjLQopaYPZ10hhBBiopI5dEIIIQZN\nKdUS9DQaI12/x3z+Ja3106PfKiGEEGLykIBOCCHEsFBKHQFu01q/0U+dMK21e/RaNT7JdRJCCDFQ\nMuRSCCHEiFFK/atS6lml1O+VUs3ADUqplUqpD5RSjUqpWqXUT4PWiQtTSmmlVLb5/H/N8leUUs1K\nqfeVUjPPta5ZfqlS6oBSyqGU+plS6l2l1C19tLvPNprlBUqpN5RSDUqpOqXUN4Pa9IBS6pBSqkkp\ntU0plaGUmq2U0t3O8Y7v/Eqp25RSb5vnaQC+q5Sao5T6m3mO00qpp5RS9qD9ZyilXlRKnTLLH1NK\nRZptzguql66UalNKJQ/+NymEEGKskoBOCCHESLsaeAZj8e5nATdwF5ACnA+sA77Uz/7XAw8AScAx\n4F/Ota5SagrwHHCved7DQFE/x+mzjWZQ9QZQAqQDc4G3zP3uBa4x6ycAtwEd/Zwn2HlABZAK/BBQ\nwL8CacB8IMd8bSilwoAtwEGMdfamAc9prTvM13lDt2vymta6foDtEEIIMY5IQCeEEGKkvaO1LtFa\ne7XW7VrrD7XWW7XWbq11JcbC3Z/oZ/8/aq23aa1dwNPAokHUXQ/s1Fr/2Sx7BDjd10HO0sYrgGNa\n68e01p1a6yatdalZdhvwba31x+br3am1buj/8vgd01r/t9baY16nA1rrN7XWTq31SbPNvjasxAg2\nv6W1bjXrv2uWPQlcby6kDnAj8NQA2yCEEGKcCQt1A4QQQkx4x4OfKKXmAT8BlmIkUgkDtvazf13Q\n4zYgdhB1M4LbobXWSqmqvg5yljZOAw71sWt/ZWfT/TqlAT/F6CGMw7gJeyroPEe01h660Vq/q5Ry\nAxcopc4A0zF684QQQkxA0kMnhBBipHXPvvULYDcwW2sdDzyIMbxwJNUCWb4nZu9VZj/1+2vjcWBW\nH/v1VdZqnjc6aFtatzrdr9MPMbKGFphtuKVbG2Yopax9tON3GMMub8QYitnZRz0hhBDjnAR0Qggh\nRlsc4ABazeQd/c2fGy6bgSVKqQ3m/LO7MOaqDaaNLwHTlVK3K6UilFLxSinffLxfAf+qlJqlDIuU\nUkkYPYd1GElhrEqpLwIzztLmOIxA0KGUmgZ8I6jsfaAe+L5SKlopFaWUOj+o/CmMuXzXYwR3Qggh\nJigJ6IQQQoy2rwM3A80YPWHPjvQJtdYngM8B/4kRCM0CPsLoATunNmqtHcCngM8AJ4ADBOa2/Rh4\nEXgTaMKYexepjTWC/gn4Nsbcvdn0P8wU4CGMxC0OjCDy+aA2uDHmBeZh9NYdwwjgfOVHgHKgU2v9\n3lnOI4QQYhyTdeiEEEJMOuZQxRrgGq31P0LdnpGglPodUKm13hTqtgghhBg5khRFCCHEpKCUWgd8\nALQD9wMuoLTfncYppVQOcCVQEOq2CCGEGFky5FIIIcRkcQFQiZEpci1w9URMFqKU+ndgF/B9rfWx\nULdHCCHEyJIhl0IIIYQQQggxTkkPnRBCCCGEEEKMU2NyDl1KSorOzs4OdTOEEEIIIYQQIiS2b99+\nWmvd3xI7wBgN6LKzs9m2bVuomyGEEEIIIYQQIaGUOjqQejLkUgghhBBCCCHGKQnohBBCCCGEEGKc\nkoBOCCGEEEIIIcapMTmHTggxcbhcLqqqqujo6Ah1U4QQYsKKjIwkKysLm80W6qYIIUaZBHRCiBFV\nVVVFXFwc2dnZKKVC3RwhhJhwtNbU19dTVVXFzJkzQ90cIcQokyGXQogR1dHRQXJysgRzQggxQpRS\nJCcny0gIISYp6aETQow4CeaEEGJkyeesEOfuxY+q+fFr+6lpbCcjIYp71+Zy1eLMUDfrnElAJ4QQ\nQgghhJhUXvyomvtfKKfd5QGgurGd+18oBxh3QZ0MuRRCCDEqfvvb33L77beHuhnjXnZ2NqdPnw51\nM4QQYtxytLv4l817/cGcT7vLw49f2x+iVg2e9NAJIcaUkR7+oLVGa43FMnL3szweD1ardcSOP2Rl\nz8GbD4OjCuxZsOZBKLw21K0a07ZUbuGxHY9R11pHWkwady25i8tzLg91s0ado6SEk488iru2lrD0\ndKbcczf2DRtC0pbs7Gy2bdtGSkpKSM4/GDt37qSmpobLLrss1E0RYtJo7XSzp6aJsqpGyqoclFU1\ncqS+rc/6NY3to9i64SE9dEKIMcM3/KG6sR1NYPjDix9VD+m4R44cITc3l5tuuon8/HysViv33nsv\nCxYs4JOf/CSlpaWsXr2anJwcXnrpJQD27NlDUVERixYtorCwkI8//pgjR44wb948Pv/5z5OXl8c1\n11xDW5vxRyE7O5tvfetbLFmyhD/84Q/s3LmTFStWUFhYyNVXX82ZM2cAWL16NXfddReLFi0iPz+f\n0tLSIb22c1b2HJTcCY7jgDZ+ltxpbB+iq666iqVLl7JgwQJ++ctfAvCb3/yGuXPnUlRUxLvvvuuv\nW1JSQnFxMYsXL+aTn/wkJ06cAGDTpk3cfPPNXHjhhcyYMYMXXniBb37zmxQUFLBu3TpcLteQ23mu\ntlRuYdN7m6htrUWjqW2tZdN7m9hSuWXQx2xtbeXyyy9n4cKF5Ofn8+yzz/Lyyy8zb948li5dyp13\n3sn69esBqK+v55JLLmHBggXcdtttaK2H66WdE0dJCbUPPIi7pga0xl1TQ+0DD+IoKQlJe8ajnTt3\n8vLLL4e6GUJMWB0uDzuPN/K794/w9ed2cckjf6dg02tc+4v3+dctFWw70kBuWhz3rs0lOSa812Nk\nJESNbqOHgQrVH4b+LFu2TG/bti3UzRBCDIOKigry8vIA+F7JHvbWNPVZ96NjjTg93h7bw60WFk9P\n6HWf+RnxPLRhQb9tOHLkCDk5Obz33nusWLECpRQvv/wyl156KVdffTWtra1s2bKFvXv3cvPNN7Nz\n507uuOMOVqxYwec//3mcTicej4cTJ04wc+ZM3nnnHc4//3xuvfVW5s+fzze+8Q2ys7P5yle+wje/\n+U0ACgsL+dnPfsYnPvEJHnzwQZqamnj00UdZvXo1c+bM4fHHH+ftt9/mK1/5Crt37x7o5Ty7V+6D\nuvK+y6s+BE9nz+3WCMha3vs+aQVw6Q/OeuqGhgaSkpJob29n+fLlvPbaa6xcuZLt27djt9u56KKL\nWLx4MT//+c85c+YMCQkJKKX41a9+RUVFBT/5yU/YtGkTb7zxBn/729/Yu3cvK1eu5Pnnn/f/rm6+\n+WauuuqqAV6Mgflh6Q/Z17Cvz/KyU2U4vc4e28Mt4RSmFva6z7ykeXyr6Ft9HvP555/n1Vdf5fHH\nHwfA4XCQn5/P22+/zcyZM9m4cSPNzc1s3ryZO++8k5SUFB588EG2bNnC+vXrOXXq1LD3TNV9//t0\nVvR9Hdp37UI7e14HFR5O1MKFve4TkTePtG9/u89jtra2cu2111JVVYXH4+GBBx4gLi6Or33ta8TE\nxHD++edTWVnJ5s2bqa+vZ+PGjVRXV7Ny5Ur+8pe/sH379l6vw5EjR1i3bh0rVqzgvffeY/ny5Xzh\nC1/goYce4uTJkzz99NMUFRXR0NDArbfeSmVlJdHR0fzyl7+ksLCQTZs2cfjwYSorKzl27BiPPPII\nH3zwAa+88gqZmZmUlJRgs9nYvn07X/va12hpaSElJYXf/va3pKens3r1aoqLi/nb3/5GY2Mjv/71\nrykuLmb27Nm0t7eTmZnJ/fffT0VFBbGxsXzjG98AID8/n82bNwMMqP3dBX/eCjHRuTxeDpxoprzK\nwa4qB+XVjeyrbcbtNWKblNhwCrMSKMi0s3CanYLMBFLjIvz7d59DBxBls/Lvny4YM3PolFLbtdbL\nzlZPhlwKIcaM3oK5/rafixkzZrBixQoAwsPDWbduHQAFBQVERERgs9koKCjgyJEjAKxcuZJ/+7d/\no6qqik9/+tPMmTMHgGnTpnH++ecDcMMNN/DTn/7U/2Xsc5/7HGB8OW9sbOQTn/gEADfffDOf/exn\n/W3ZuHEjAKtWraKpqYnGxkYSEnoPWIddb8Fcf9vPwU9/+lP+9Kc/AXD8+HGeeuopVq9eTWpqKmBc\nnwMHDgDG+oSf+9znqK2txel0dlk769JLL/X/PjweT5ffle/3M5p6C+b62z4QBQUFfP3rX+db3/oW\n69evJy4ujpycHP912Lhxo7+X8+233+aFF14A4PLLLycxMXHQ5x2K3oK5/rYPxKuvvkpGRgZbthi9\nnb0Ftj7f+973uOCCC/yB7a9//et+j33w4EH+8Ic/8MQTT7B8+XKeeeYZ3nnnHV566SW+//3v8+KL\nL/LQQw+xePFiXnzxRf76179y0003sXPnTgAOHTrU48bCj370I66++mq2bNnC5Zdfzh133MGf//xn\nUlNTefbZZ/nOd77DE088AYDb7aa0tJSXX36Z733ve7zxxhs8/PDDbNu2jZ///OeA0SM9lPYLMVl4\nvJrDp1vYddxBebWDXVWN7K1potNtfD+IjwyjMCuBL67KoTDLTmFWAun2yH6zv161OJPM45uZtuPH\nTNGnOKlSOb7kXpYvXjdaL2vYSEAnhBg1Z+tJO/8Hf6W6l7HrmQlRPPullUM6d0xMjP+xzWbzf8hb\nLBYiIiL8j91uNwDXX389xcXFbNmyhcsuu4xf/OIX5OTk9PjjEPw8+Bz96e8YQ3a2nrRH8s3hlt3Y\np8EXBj+E8K233uKNN97g/fffJzo6mtWrVzNv3jz27t3ba/077riDr33ta1xxxRW89dZbXb7YBv8+\nuv+ufL+f4dRfTxrAJX+8hNrW2h7b02PS+c263wzqnHPnzmXHjh28/PLLfPe732XNmjWDOs5w6q8n\nDeDji9cYwy27CcvIYMZTvxvUOUcysJ05cyYFBQUALFiwgDVr1qCU6nJj4J133uH5558H4OKLL6a+\nvp6mJmMUwdluLOzfv5/du3fzqU99CjDmzqanp/vP/+lPfxqApUuXDupGxEDaL8REpLXmWEObf75b\nWZWD3dUOWp1GT1p0uJX8TDs3rphB4bQECjPtzEiOPve/pWXPsbz8IaAdFKRxirTyhyA7cdzNK5eA\nTggxZty7NrfX4Q/3rs0d9bZUVlaSk5PDnXfeybFjxygrKyMnJ4djx47x/vvvs3LlSp555hkuuOCC\nHvva7XYSExP5xz/+wYUXXshTTz3l760DePbZZ7nooot45513sNvt2O320Xthax405sy5ggJnW5Sx\nfQgcDgeJiYlER0ezb98+PvjgA9rb2/n73/9OfX098fHx/OEPf2ChOTTP4XCQmWkMaXnyySeHdO6R\ndteSu9j03iY6PIFFmyOtkdy15K5BH7OmpoakpCRuuOEGEhIS+NnPfkZlZSVHjhwhOzubZ5991l93\n1apVPPPMM3z3u9/llVde8c/HHG1T7rmb2gceRActXq0iI5lyz92DPuZIBra+GwPQ942bgezf140F\nrTULFizg/fff73d/q9Xa5/nCwsLwegMjEIIXBh9q+4UYD7TW1DV1mD1vvqQlDhztxnzp8DAL89Pj\nuWZpFgVZCSzMspOTGovVMsQboVrDGw91/VsIxvM3H5aATgghBss3Zn0sLPL53HPP8dRTT2Gz2UhL\nS+Pb3/42TU1N5Obm8l//9V/++XP//M//3Ov+Tz75JF/+8pdpa2sjJyeH3/wm0JMTGRnJ4sWLcblc\n/uFZo8b3R2qYs1yuW7eO//mf/yEvL4/c3FxWrFhBeno6mzZtYuXKlSQkJLBo0SJ//U2bNvHZz36W\nxMRELr74Yg4fPjyk848kXzbL4cxyWV5ezr333usPFv77v/+b2tpa1q1bR0xMDMuXB+YzPvTQQ2zc\nuJEFCxZw3nnnMX369CG/psHwZbMcziyXoQ5sL7zwQp5++mkeeOAB3nrrLVJSUoiPjx/Qvrm5uZw6\ndcp/g8flcnHgwAEWLOh7JEJcXBzNzc3+59nZ2f45czt27BjT/w+EGA6nWzrNOW+N/rlvp1uMIf9h\nFsXcqXFcVpBGQWYChVl2ctPisFkHkcPR3QlN1dB43Phb56gCx7Ggx1Xg7uh9X0fVEF5haEhAJ4QY\nU65anDnsAVx2dnaXxCMtLS3+x93nsPjK7rvvPu67774uZU1NTYSFhfG///u/Pc7RfQjUokWL+OCD\nD3ptzw033MCjjz56Li9heBVeO+x3HyMiInjllVd6bF+9ejVf+MIXemy/8sorufLKK3ts7+v30VvZ\naLo85/JhXaZg7dq1rF27tsu2lpYW9u3bh9aar371qyxbZsyDT05O5vXXXx+2cw+FfcOGYV2mINSB\n7aZNm7j11lspLCwkOjr6nHqLw8PD+eMf/8idd96Jw+HA7XZz99139xvQXXTRRfzgBz9g0aJF3H//\n/XzmM5/hd7/7HQsWLKC4uJi5c+cO+TUJMVY42l2UVzkoq26kzJz75ptWoRTMTo1l1dwUFmYlUJBl\nZ356PJG2ASz5ozW0nzEDMzNgawwO1o5Dy4me+8WmGTcx0wog91LY8RR0NPasZ88a4isffZLlUggx\noiZS1rUjR46wfv36IWWlXL16Nf/xH//h/7IuhM8jjzzCk08+idPpZPHixTz++ONER0eHulmjrqWl\nhdjYWH9gO2fOHO65555QN2tcmEift2J8OdtabzOSoynMMua7FWbZWZBpJzaij34ljwuaa4N6144H\nAjdHlbHd1dp1n7BIIxCzZxlzwu3TIGFaYFt8JoRFdN3Ht4xP9ykIG346ZoZcDjTLpQR0QogRJV8w\nhBDnQgLbwZPPWzEaOlweKmqbjGyT5ty3gydbMFcLIMMeSYGZabIwy05Bpp2E6KA13zqaegZqwcFb\ncy3obtmto1OMwCzBDNb8gZv5MybF6PY7V2XPDfsUhOEkAZ0QYkyQLxhCiJFSX1/fayKVN998k+Tk\n5BC0KLTk81YMN99ab75kJWVVjeyv62ett/Q4UlWjGaAd7zos0he4dTq6nsRiA3tmoGfN16vmC97i\nMyF8ct7UkXXohBBjhtZ6eFPzCyEExvxC37pxk91YvEEvxhePV1N5qiWwXEC1o8dab8szIrh6uYVF\ncU3MjnBgd9ahHFVQVQV7jkFTDXi7ZWGNTAgEazPO6zos0p4FsVPBMojEJ8PAUVIyrImeQkUCOiHE\niIqMjKS+vp7k5GQJ6oQQYgRoramvrycyMjLUTRHjhG+tt11VDsqrGtlV5WBPdSNRzjNkqlPMtJ3h\nKnsL92c4mGatJ8l9kvCWalRNAwQvR6msEJ9hBGbTVgT1rk0PPI6IC9nr7I+jpKTLUizumhpqHzCW\n8BlvQZ0MuRRCjCiXy0VVVVWX9ZWEEEIMr8jISLKysrDZbKFuihhjfGu9lR85ydHDBzhdfYj2U0dJ\ndJ0gU50my1LPDFsDU7ynsWln153DYwM9aQnTevauxaWDdXz1D3k7Omj/6COq7rgTb1AmZZ+wjAzm\n/PXNELSsJxlyKYQYE2w2GzNnzgx1M4QQQoiJy5/K/zhNdZXUHT9Ic10lurGKyLZapuqTXKK6zl3T\nNoU7egrWxOlYEoq7Bmq+4C0yYXDJRsYQb2cn7Tt30bZ1K62lW+nYVYZ2ufqs766tHcXWDQ8J6IQQ\nQgghhBjLPC5jflpwopHG47jOHMdVfwxbSzU2r5F+P978167DOWVNpT0mnTMJBbimZpOSOYvwpBlg\nz0LFZ2Drnsp/AtBOJ+1lZbRu3Urb1lLad+5EO51gsRA5fz6JN91ITHExtQ8+hLuursf+YenpIWj1\n0EhAJ4QQQgghRCh1OILS93fLDtlopvKn6zSpM9g55k2mRidTrefQHp1OMHIP4gAAIABJREFUVEo2\nyRmzmJ4zl9ycmUyPnPhDcLXLRXv5btpKS2kr3Urbjo+MeXFKEZE3j8Trrye6qIjoZUuxxsf795vy\n9a91mUMHoCIjmXLP3aF4GUMiAZ0QQgghhBAjxeuB5roevWv+VP6O49DZ1GUXbbHhjMmgwTaFKksh\n+yJWsacljiqdQrVOQcdlkDttin+tt2u6r/U2gWm3m469e/09cG07dqDbjEXMI3JzSbj2s8QUFRG9\nbBnWhIQ+j+NLfDIRslxKUhQhhBBCCCEGy9nad++a43jfqfzNddY88VmcsqRy0JlAWXM8752OYutJ\nKy6vMXetx1pvmQmkxk28oZJ90R4PHRX7/HPg2rdtx9vaCkD47FnEFBUTXVxMdNFywhITQ9za4SVJ\nUYQQQgghhBgKrxdaT5kBWtAQSH/QdtxIRhJMWY3FsH2p/IOyQ3riszjsTGDXSU9grbc9Xdd6K8xK\n4J/m2SnMslOYlUC6PXJSLfujvV469+83euBKP6Rt2za8TUYPZvjMmcRvWE9McTHRy5cTlpIS4taO\nDRLQCSGEEEKIycnVAU3V0His6xBIR9CQSE/3VP5xgSAta1nP7JCxaWAN67nW23YHe6qP0uqsBCA6\n3Ep+pp0bV8ygcFoChZl2ZiRHT6rgDcwA7uBBY/hkqRHEeRxGRk7bjOnEr11rzIErKsI2dUqIWzs2\nSUAnhBBCCCHGnrLn4M2HjaDKngVrHoTCawe+v9bQ1tBL71pQD1vrqW47KYhLMwK09EUwb33QItm+\nVP72Hqn8tdbUOjooO+6grOog5dUOyqocONqN9PjhYRbmp8dzzdIsCrISWJhlJyc1FqtlcgVvYFwr\nZ2VlYA5caSmeM0Yvpy0ri9g1a4gpNgO4cZhxMhQkoBNCCCGEEGNL2XNQcie4jFT8OI4bzyEQ1Lmd\n0FzTLcFIcE9bFbjauh43LCrQu5aWD3ZfsGb2rsVlQNjZk4ucbuk0hkxWOfz/Trd0GqewKOZOjeOy\ngjQKMo2kJblpcdisluG6OuOK1hrnkSP+HrjW0g/xnD4NGEsExK5aZc6BKyI8KzPErR2fJKATQggh\nxOAMtQdFjD9am/88oL1GBkftDTzXuuc2/3Nv7/t4PeYxg7a99u1AMOfjajeCutLHjQCvuY7uqfyJ\nSTV60lLnwexPBc1fyzKCt+ikc14o29Hmorzawa6qRsqrHJRVNVLjMFLdKwWzU2NZNTeFhVkJFGTZ\nmZ8eT6TNOoSLPL5prXEdPx6YA7d1K+6TJwEImzKFmJUrAz1w06ZNuiGmI2FIAZ1Sah3wGGAFfqW1\n/kG38luAHwPV5qafa61/NZRzCiGEEGIMGEgPymB5vX0EBJ7eA4YBBQ3efvbp47h9HrOf4w64rf0E\nRf22dYCvr3v94dpHe4f2ux0qVzvYImHWxV2HQdqngT0TbFFDOnxrp5s9NU1BvW+NHKkP9PLNSI5m\naXYSX8g0kpYsyLQTGyH9I86qamMduK1baS0txV1bC4A1JYWYouVEFxUTXVxEeHa2BHAjYNDLFiil\nrMAB4FNAFfAhsFFrvTeozi3AMq317edybFm2QAghhBijtDbSsP9iFbSd7lluCYOEGQMPGnoLkiYc\nBcoCFqvxU5k/LZZuz33lQf/Otk9v+/XYx2p0JfW6j1nW6z6WrvsNS/sH2Jbnb+tlfhtG4HbP7mH5\nrXS4PFTUNhm9b8cdlFc3cvBkC17zq3GGPZICM9NkYZadgkm01tvZuOrqjOBtqxHEuaqNvhtrYqKR\nwKS4iJjiYsJzciSAG4LRWLagCDiota40T/h/wJXA3n73EkIIIcT4oDWcOQK1u7r+6y2Q8/G6IXNJ\nH1/8+/ny32ewcC77DCIIGfGAyHLOQ/wEsPb7XXuAweh9W/PgoA7n8ng5cKI5aM5bI/vrmnGb0Ztv\nrbdL89Mn5VpvZ+M6eTJoDlwprqPHALDa7UQXLSfplluILi4iYvZslGVyzhUMpaEEdJnA8aDnVUBx\nL/U+o5RahdGbd4/W+ngvdVBKfRH4IsD06dOH0CwhhBBCnDOvB+oPmUHbTuNnXRl0GOnDsYTBlDzI\nXWdk//v7j6D1ZM/j2KfBZ2R2hRgi37DdQczR9Hg1lada/IFbWbWDvTU913r74qqcSbvW29m4T5+m\n7cMP/ZkonYcPA2CJiyN6+XKSrr+e6KIiInJzJYAbA0Z60G8J8HutdadS6kvAk8DFvVXUWv8S+CUY\nQy5HuF1CCCHE5OVxw6l9XXvd6srB1WqUWyOMDID5n4H0hca/KfMhLKjHItI+rD0oQnT3oud8ftz5\nU2o62smIjOJeTy5XdavTY623Kgd7qh20Oj2ArPU2UO4zZ/wJTFpLt+I8eAgAS0wM0cuWkXDNNUQX\nFxOZNw9lnbwJX8aqoQR01cC0oOdZBJKfAKC1rg96+ivgR0M4nxBCCCHOlbsTTu7tGryd2ANuI0sf\nthhIL4QlNwaCt5S5YLX1f9wh9KAIcTYvflTN/S+U0+4yArPqxnbuf6GMM21O0u1RlFU1ylpvQ+Bx\nOMweOGMOXOeBAwCo6GiilyzBfuWVxBQXEzl/PipMkr6MdUP5DX0IzFFKzcQI5K4Drg+uoJRK11rX\nmk+vACqGcD4hhBBC9MfZagRrwcMmT1YY89oAIuxG8Lb8NshYbARvSTnGfK9BGEgPihiftNZ4vBqP\n1ni94PZ68XrBo3WXx16vxu016nq1xu0xfvr29fjKfPXMfXzbg+v4juHxwg9f3ecP5nzaXV6+V2Kk\napC13s6Np7mZtg+30VZaSmvpVjor9oHWqMhIopcsJv6yu4kuLiIqPx9lO8vNHDHmDDqg01q7lVK3\nA69hLFvwhNZ6j1LqYWCb1vol4E6l1BWAG2gAbhmGNgshhBCiw2EMkwzueTt9IJAlMjrZmOt23qcC\nPW+J2cOWoKP3HpRyAK5aPHYWB+4emHi0xuPpFkgEBRlub1BA0q3cbQYmRlAT2Mer+wpqfOfz4tEE\njtHP8T19lHv6LAOP12sGTfRoW5fX1z3gCjp297Z5x/Dklxe+ct6kX+vtbDwtrbTv2O6fA9exdy94\nvajwcKIWLybl9q8aPXCFhVjCJXPneDfoZQtGkixbIIQQQgRpawj0uPn+NVQGyuMyAkGb7198xrBn\nV+x0e2hodVLf4uTmJ0qpb3X2qBMbEcZ1y6d1Cw6MQKJLYOQPKsyAxB/wBHp/uvfcBAdUvZV5eikf\ny4EJgEWB1aKMf0phsSjCzOcWpQJlZrl1IGUWhVWB1WLBasFfP8xfpgizBo7hK/PtG9bH8f1l/vMF\nzuGrP/B9u76WsC5tV1z1X+9S19TR43plJkTx7n29pmOY1LxtbbTt+Mg/B65j9x7weMBmI2phITFF\nxUQXFxO1aCGWCMneOV6MxrIFQgghhBhuzSeCAredUFsGjmOB8oQZRsC26PNGD1x6IcROGdSpggO0\n+lYnDa2dgcfmz/rWThrM582d7rMes6XTze9Lj/X6hd3SR2ASHFz4ysItlm7BhS/wsWBV9B34KHXO\ngUmgbZhtDQQpvbUtOPDocuwBnx/CLBYsCknO0Yf7Lp3XpQcYIMpm5d61uSFs1djh7eig/aOPjB64\n0g9pLy8HlwvCwogqKCD5n24jpriYqEWLsEQNbbF1MfZJQCeEEEKEgtZGMpHua7y11AXqJM+Gacuh\n6J+MIC6tAKKT+jzkcAVoNqsiKSacpJgIkmPCmZ4UTVJMOMkx4STHRpAUE853/lTO6ZaePXTSgyKG\ng2/Y7o9f209NYzsZCVHcuzZ3TA3nHU3ezk7ad+6ibetW2kpLad+1C+1ygdVKZP4Ckm+5hejiYqIX\nL8ISExPq5opRJgGdEEIIMdK8XjhzuGfw1t5glCsLpM6DWRcFhkxOzaczLKZrgLa/jfqWxhEJ0IIf\nx0eGnbXnqN3pkR4UMaKuWpw5aQM47XTSXl7unwPXvnMnurMTLBYi588n8aYbiSkqImrpUqyxsaFu\nrggxCeiEEEKI4eT1QP1BI2CrCVqgu7MJAG2x0ZGUS2PGJzkRk8vR8DkcVDM42W6hvslJfW0nDa2d\nNLS8P6oB2rmSHhQhho92uWjfvZu2raW0lW6lbcdH6I4OUIqIefNIvO46owdu2VKs8fGhbq4YYyQp\nihBCCDFInZ0dNB3fTeexj1B1u4g8XU584z5sXiOZg1OFc9iaw15m8pFrBtud0zmgs3B1u5/aPUBL\njg0f9QBNCDF6tNtNx969/jlwbdu3o9vaAIiYO5fo4mJiiouIXrYMa0JCiFsrQkWSogghhBDnqL85\naE1NzUQ17ie5uYLM9gPkuA4xh6OkKqMXrUVHskdns8e7mgpmcjxyLs2xOSTGRvkDtEtjwrlBAjQh\nJh3t8dBRsY+2UmMh77bt2/G2tAAQPnsWCVddZfTALV9GWFLf82SF6I0EdEIIISaswSYJiaaDPHWU\nfMsR8tVhzrceYY6qIgxjjbdWSxwn4nPZYz+P9pQCvGmFRE2dw5S4KOZJgCbEpKe9XjoPHDCWEdha\nStu2bXibjGHX4TNnEn/55UYPXFERYSkpIW6tGO8koBNCCDFujEQWx2lRTi5OOkpu4iFmdB4krW0/\n8a1HUBhTErzRqaiMRaj0a/0JS2ISppMjAZsQwqS1pvPjjwNz4Eo/xONwAGCbPp34tZcQXVRMdFER\ntqmDW2ZEiL5IQCeEECJkRjPNfnJMOKnWFlKa9xFdvxvlyzRZczhwoPgsyFoI6Rv9wZslLm3YF+gW\nQoxvWmuclZW0lZYaPXClpXgajKy1tsxMYtes8ffA2dLTQ9xaMdFJQCeEEGLYjHaA1uccNK2huS6w\nPMAh82dTVaBOYrYRtC250QzeFkGMDH0SQvSktcZ19KgRvG3dSuuHpXhOnQYgLD2d2AsvJLqoiOji\nYsKzJNOrGF0S0AkhxAT24kfVQ0orP2YCtP5oDY3Heq7x1nrSrKAgZQ7MWBlY4y2tAKISB3wdhBCT\ni9YaV1VVYA5caSnuEycACEtNJaZ4BdHFRcQUF2ObNk3mzIqQkoBOCCEmqBc/qu6y8HN1Yzv3vVBG\nY7uTZTOSxm6A1h//At07A2u81e6CjkajXFlhSh7M+VSXBbqJkIV3hRD9c1VXd+mBc9fUAmBNSSGm\naLkxB664iPDsbAngxJgiAZ0QQkxQP3x1nz+Y8+lwedn00t4edcfCQtU9eNxQ/3G3nrcycDYb5dZw\nmLoAFlwVCN6mzAdb1Mi2SwgxIbjq6ozgrbSUtq2luKqMIdnWxERj+ORttxFTXEx4To4EcGJMk4BO\nCCEmkA6Xh7f2n6RkVy21jo4+6/3ixqVjax00txNOVXQN3up2g7vdKA+LMoZJLrzOCNwyFkFKLoSF\nh67NQohxxXXypLGI99attJZuxXX0GAAWu52YouUk3XQT0cXFRMyZjbJYQtxaIQZOAjohhBjnnG4v\n7xw8RcmuWv6y9wQtnW5SYsOJCbfS6vT0qJ+ZEMXaBWkhaKnJ1Q4n9hjDJn3B24m94HUZ5RHxkFYI\ny24N9LylzAGLNXRtFkKMO+76ejML5VbatpbiPGxktLXExRG9bBmJGzcSU1xMRG6uBHBiXJOATggh\nxiG3x8vWww2U7Krhld11ONpd2KNsXF6QzoaFGazISWJzWW2XOXQAUTYr967NHb2GdjYbPW21uwIB\n3Kn9oM02RSUa2SVXfjUQvCXOBPlyNS44Sko4+cijuGtrCUtPZ8o9d2PfsCHUzRKTlPvMGX8PXNuH\npXR+fBAAS0wMUcuWknDNNUQXFxOZNw9llRtEYuKQgE4IIcYJr1ez/dgZSnbV8HJ5LadbnMSEW7lk\nQRobFqZzwexUwsMCgZAvm+VQslyek/Yzxhy34GGT9QfBXKCb2KlG8DZvfSB4s2fJGm/jlKOkhNoH\nHkR3GEN73TU11D7wIIAEdWJYnO2GgcfhoG3bNn8PXOf+/QCo6Giilywh/ooriCkqInLBAlSYfOUV\nE5fSWoe6DT0sW7ZMb9u2LdTNEEKIkNNaU1bloGRXDVvKjXlxEWEWPpk3lfWF6Vw0bwqRthDcaW45\n1bXXrXYXNB4NlNunBYI237+4EA7zFMPu44sv9mcBDGaJjyf1jjvAooxhbEqBshjP/Y8toDDLjTrK\norrUNbb5ys26Fgugeh67W7myKPMcRrn/2OY2pYLLg45lsZht9JV3O7Zv36BypTAe93VsuWExKN1v\nGACoyEgSNl6H0tBaupXOin2gNSoigqgli4kpLia6qJiognyUzRbC1gsxPJRS27XWy85aTwI6IYQY\nW7TW7KtrZnNZDSW7ajnW0IbNqvjE3FQ2LMxgTd5UYiNG6W6z1tBU03ONt+aaQJ2knK6BW9pCiEke\nnfaJUeNuaKCjvJz28t20l5fR+ve3Q92k8SEoAOwaDHYL/rqXWxQKFRQsglL91O0SLHc/dh/H8tW1\n+AJjs27Qec5WriwKCLTDOHdQXX8QfvagPvjYDb/7Hd7m5t4vaXg4UYsW+deBiywsxBIuCZLExDPQ\ngE76n4UQYoyoPNVCya5aSspqOHiyBatFcd6sZG6/eDZr56dhjx7hO85aG71stbu6rvHWdtooVxZI\nmQszL+y6QHekfWTbJUadp6WVjr17/AFcR3k5rupqo1ApwmfloKKi0O3tPfYNS5vKzBdeMN5PXi/a\nqwHjMVobz7W313Lt9RojdLWvrhf6Kvd60Vp3K9fGcbWvrg6cx1e3S3kvx+rl3P5jdSk3XxNBdX3H\n9tXtq9wbaJdRHnR9dPe6Zjv7LA9+jf3U7V7u9aI9bvBqs71mu/uo629nX8ci6DVr3eVYRnnQsXTg\nnL3VPZu5H5ZiiYgY0ntciIlEAjohhAih4w1tbC6rZXNZDXtqmlAKlmcn8S9X5XNpfhopsUP80lL2\nHLz5MDiqjPlqax6EwmuNL00Nh3oOm+xwGPtZwowFunPXGfPe0hcaa76Fxwz9RYsxxet00rl/P+3l\n5XSYvW/OQ5XGF2zAlpFBZEEBiddvJDK/gMgF87HGxvY5JG7K179OWFJSqF6OmAC01hy8eA3u2p5D\nesMyMiSYE6IbGXIphBCj7ERTB1vKjJ64j441ArBoWgIbFmZweUE6afbI4TlR2XNQcqexTICPJQwS\nsqGlDpwtxjZrhBGsBQ+bnDIfbMPUDjFmaI8H5+HDZq9bGe3lu+nctw/tMpaMsCYmEllYQFR+AZEF\n+UQVFBCW3PfwWclyKUZKXzcM0v/lYXmPiWGzpXILj+14jLrWOtJi0rhryV1cnnN5qJvlJ3PohBBi\nDKlv6eSV3XVsLqth6+EGtIb56fGsX5jOhsIMpiVFD+0EWkPLSSOrZP3Hxs/Sx8Hdy+Li1nBY+oVA\n8JaaC1ZJIDDRaK1x19TQXl7u733r2L0bb1sbAJboaCIXLCCyoICowgIi8wuwZWZIEg8xZsgNAzGS\ntlRuYdN7m+jwBP5ORloj2XTepjET1ElAJ4QQIeZod/H6njpKymp59+BpPF7NrNQYNizMYH1hBrOn\nxJ77QZ2tUH/ICNpOHwwK4A5BZ1OgXlhk78EcAAo2NQ7qNYmxq3vSko7y3XgaGoxCm43I3Fx/4BZV\nkE94To6sxSWEmNBcHhdNziaanc00O5v9j5ucTTy6/VGaXT0T76THpPP6Na+HoLU9SVIUIYQIgdZO\nN29UnKBkVy1vHziF0+NlWlIUX1qVw/rCDPLS487eA+L1QOMxI1g7/XHXoK2pOqiiMpYHSJ4FC6+D\n5NmBf/Zp8FghOI73PL49a1hfsxh9A0laErtqlTF8sqCAiNxcyQIohBh3vNpLq6u1azDW2dQlMAv+\n6Xvse97u7pm46WzqWutG4JWMLAnohBBiiDpcHt7af5KSslrerDhBh8tLWnwkN66cwYaFGSzMsvcM\n4rSGtvpuQZv5r6ESPM5A3Ug7JM+BmauM4C15jhm4zQJbVN8NW/Ngzzl0tihjuxg3BpS0pLAwKGnJ\nAqyxkrxGCDE2dLg7egRbXQKwziaaXebPbgFZi6sFr+4786lCERseS3x4PPHh8cSFx5Edn01ceJz/\neXxEvP+5f1t4PNdvuZ66tp7BW1rM+FszVQI6IYQYBKfby7sHT1Oyq4bX956gpdNNckw4n106jQ0L\nM1g2IxGLRRnB1Mm9PYO20x9DR9CwR4vNWM8tZQ7MXRsI2lLmQHSysUbTuSq81vjZW5ZLMSadNWlJ\nUhKRBfnEr11nDp/M7zdpiRBCDJXH66HF1WL0jLma/IFX9x6yHr1mZj2n19nv8aPCooizxfmDr9To\nVGYlzOoalAUHY2aAFhceR6wtFouyDOp13b307l7n0N215K5BHS+UZA6dEEIMkMer+aCynpJdNby6\np47GNhfxkWFctmAqn54NS2NOYz1TGUhKcvqgOeQx6HM2LgNSfEMjfUHbbLBPB6vcY5tMBpy0xBw2\nKUlLhBCDobWm3d3eNegye8WChzD2Ndes1dXa7/Gtytoj+PIFXPER8T16xvxl5uNwa+iGg0uWyxEk\nAZ0QYqzwejU7jp2hZFcNb5cdJKHtCPNsJ7gotYlFUadIcR7H0lDZNQFJeFzXoM33OGkWRAwiEYqY\nEM6atGTePKIK8o2kJYUFhM+cKUlLhBBA1+Qevc0X6zGEsVs9t3b3e/wYW0yvQVmPnjFbz0AtKixK\nbjSNEEmKIoQQg+HuRDdUcvRAGZX7dtJWs4+p7iruUrV8TzWDbz3bM2FAthGozbrYGBrpC+Bipwxu\niKSYMCRpiRAimC+5R/fhiL0NVewxbNF19uQeNoutS+Blj7QzLW5al/ljwT1jwYFabHgsYRYJCcYz\n+e0JISYfraGpJpA90kz/7zx5gLCm41jwkg1kA43WJNypOcRNWwFT5gaCtsQZsnabACRpiRCThS+5\nR6/zxnzzynpJ7tHkbKLF2YKm71FxA0nu0dsQRl95hDVCeskmMQnohBATV0dT1yQkwen/XW3+ai5L\nJEdJp8KVzmG9FNuUOczOW0zRsiISEiXhhAiQpCVCjJ7hnt/k8Xp6TW3f288egdoAkntEWiO79JIF\nJ/forWcsOMHHUJJ7CCEBnRBifPO44MyR3tP/t5wI1FMWSJgOyXNoTlvBR20pvFoXy19PxnNCJbE8\nO5kNhelcX5BOSmxEn6cTk8dAk5Yk3nSjJC0RYphtqdzSJQNhbWstm97bRKe7k/Myz+sxJHEgQxgH\nk9wjLTqt356xsZLcQ0xukhRFCDH2aW0EZ70FbWeOgDdosnd0ctdEJGYmyZNh6WypaKBkVw07jhnL\nBSyclsCGwnQuL0wn3d7Pem5iUpCkJUKEntaaU+2nuOalazjTeeac9++e3KPPnrGgnjRJ7iHGKkmK\nIoQYfzpboOGQGbQdCqT/rz8EnU2BemGRRsbIqQtg/pVdF9qOTvJXa2h18sruWkr+UcPWw4fQGvLS\n4/nmulzWF2QwPTk6BC9SjAWStESI0NNaU9NaQ0V9BXvr91LRUEFFfQX1HfX97vfQyockuYcQQeRd\nL4QYXR43OI75E5EYSUnMAK65JqiiAvs0o6dt4XVm0DbLyCYZnwWW3ucaONpdvL6njs1ltbxz8DQe\nryYnNYY7L57DhoXpzJ4SNzqvU4wZkrREiNDzai/Hmo75g7a9DXupqK+gyWncrLMqK7MSZnFB5gXk\nJefxeNnjvQZ26THpXDP3mtFuvhBjmgR0QojhpzW01ZuBWtAi2/UHoaESvK5A3cgEI0jL+YTZyzbb\neJ6UA7aBDYNsc7p5o+IkJbtq+Pv+Uzg9XrISo/jiqhw2FGaQlx4nw2gmCUlaIkToub1uDjsOB4K3\n+r3sa9hHm9uYf2qz2JibOJdLsi8hLymP+cnzmZM4hwhrYP5yQkRClzl0YCQduWvJXaP+eoQY6ySg\nE0IMnqu969DI4F63DkegnjXcCNBS5kDupYGgLXmOMURyEMFWh8vDW/tPsbmshjcrTtLu8jA1PoIb\nV85gw8IMFmbZJYib4AaUtCQ/35+0JKqggLAMSVoixHByeVwcbDxIRUNg2OSBhgP+QCwqLIrcxFyu\nnH2lP3jLScjBZul/2RdfNsvhzHIpxEQlSVGEEP3zesFxPJDuPzgpieN417rxmcawyOQ5QQttzzay\nS1qGnjzC5fHyzsHTlOyq4S97TtDc6SY5JpxLC9LYUJjB8uwkLBb5sj5R9Ze0RNlsRPiSlhQUElWQ\nL0lLhBhmHe4ODpw5QEV9hT+A+7jxY9xmYqpYWyzzkuaRl5znD96y47OxDsPnvxCT0agkRVFKrQMe\nA6zAr7TWP+ij3meAPwLLtdYSqQkxFrU19LJmm5mQxNMZqBceZ8xrm74SUm4KBHDJsyB8+Ocdebya\nrZX1lJTV8MruOhrbXMRHhhlB3MIMVuYkE2aVtXsmmgElLfnEJ4gsyJekJUKMgFZXK/sa9nUJ3g47\nDuPRHsAYEpmXlMdN828iLzmP+UnzyYrLkrXUhAiBQQd0Sikr8F/Ap4Aq4EOl1Eta673d6sUBdwFb\nh9JQIcQwcHcac9j8QduhQFKS9oZAPUsYJGYbgdrsNV3S/xM7ZVBDJM+F16vZcewMm8tq2VJey6nm\nTqLDrVwyfyrrCzO4cG4KEWFyx3ei0E4nHfsP+HvdOnaX03nwUCBpSWYmkQUFRtKSggIi50vSEiGG\nk6PT4Z/v5gvgjjYdRWP8H0yNSiUvOY8109f4g7e0mDQZvizEGDGUHroi4KDWuhJAKfV/wJXA3m71\n/gX4IXDvEM4lhBgor9fIFtlb0OY4DtobqBubZgRp86/oGrQlzgBr//MbhpvWmt3VTZSU1bB5Vw01\njg4iwixcPG8KGxZmcFHuFKLCJYgb7waatCRu7Tpz+GQBYUlJZzmqEGKgTref7tHzVt1S7S/PiMkg\nLzmP9Tnr/UMnU6NTQ9hiIcTZDCWgywSCJ9BUAcXBFZRSS4BpWustSikJ6IQYTh2OoEQkQUlJGg6B\nqy1QzxZjDIfMWtY1/X/ybIiMD137TfvrminZVcPmshqO1LdhsyoZBNqHAAAgAElEQVRWzUnlm+vm\n8cn5U4mNkNxN45UkLREidLTWnGg74Q/cfEsFnGw76a8zPW46+Sn5fHbuZ5mfPJ+8pDwSIhNC2Goh\nxGCM2DclpZQF+E/glgHW/yLwRYDp06ePVLOEGHvKnoM3HwZHFdizYM2DUHitUeZ2QuPR3tP/twb+\nKKMskDDDSEQy88Ku6f/j0kd8iOS5Ony6lc27aigpq+HAiRYsCs6blcI/r57F2gVpJETLXKjxaCBJ\nS+xXXSlJS4QYZlprqlqqugRvFQ0VNHQY//8sysLM+JkUpRWRl5RHXnIe85LmERcu63IKMREMOsul\nUmolsElrvdZ8fj+A1vrfzed24BDQYu6SBjQAV5wtMYpkuRSTRtlzUHKnkf7fxxIGqXlGL9uZI2BO\nQAcgOsXMHjkrMDwyZQ4kzoSwsR0EVZ1pY0tZLSVlNeyuNhaSLcpOYsPCdNblp5MaF3GWI4ixZCBJ\nS6IKCiVpiRDDzOP1cLT5aJf5bhUNFTQ7mwEIU2HMTpztD9zykvKYmziXaFt0iFsuhDhXA81yOZSA\nLgw4AKwBqoEPgeu11nv6qP8W8I2BZLmUgE5MGo/k90z9D2CxwbzLugZtybMgKnH02zgEJ5s7eLms\nlpKyWrYfPQPAwmkJbChM5/LCdNLtA1s4XITWQJOW+Oa8SdISIYaH2+vmUOOhLr1u+xr20e42bgKG\nW8LJTcoNBG/JecxJmEO4VW6eCDERjPiyBVprt1LqduA1jGULntBa71FKPQxs01q/NNhjCzEpaN17\nMAfgdcO1vxvd9gyThlYnr+6uo2RXDR8crkdrmJcWx71rc9lQmMH0ZLlLPJZJ0hIhQsPpcfJx48dd\net4OnDlAp7lsTFRYFPOS5nH17KuNTJPJ85lpn3nWBbqFEBPfkObQaa1fBl7utu3BPuquHsq5hJhQ\nnG3w56/2XW7PGr22DIOmDhev7znB5rIa3vn4NG6vJiclhjsvnsOGhenMniLzNELFUVLCyUcexV1b\nS1h6OlPuuRv7hg2AJC0RIlTaXG3GAt1BPW8HzxzErY0FuuNsceQl53Fd7nX+nrcZcTNkgW4hRK8k\nfZwQo81RBf93PdSWwYJPw4FXus6hs0UZiVHGuDanmzcrTlKyq4a39p/C6fGSlRjFbRfmsGFhOvPT\n4+WLf4g5SkqofeBBdEcHAO6aGmq/812aXv8L2tkpSUuEGAXNzuYuywRU1FdwuOkwXnMJmcSIROYn\nz+eC/Av8QyezYrPk81MIMWAS0Akxmo59AM/eYCzwff2zMHdt/1kux5gOl4e/HzhFya4a3qw4SbvL\nw5S4CG5YMYMNC9P/P3v3HSZVdf9x/H13tvdd2AoLS12KwDbAXhBYDaJGLKDYolHEAjGaprFgjSYq\nGmvUxJ+oWKJGRF1ssUfZQi/Slrq9992ZOb8/BicSQMrMMls+r+fxkblz59zv6LDPfPbcc76kp0Tr\nS0gnYRwOSh940B3m3Mfb2mj48EMCBw8i/KSTtGmJiBfVtNSwpmrNHuFtW/029/PxofGMiB3B5NTJ\n7vCWEJqgn5si4pHD3hSlI2lTFOmW8l+Axb+G6BSYsRDi0nxd0UFpdzj5amMFi5YXs2R1CfWtdmLD\nAvnZqETOGJ3M2NRYbH76MuJrzuZmmlespLkgn6b8ApoLC3E2Nu77ZMti+No1R7ZAkW6mormCNZV7\nhrddjbvcz/cJ7+MObSN6jWBY7DB6h/T2YcUi0tV0+KYoInKQHO2Qewt89zQMmgDnPt/pd6t0OA3f\nbqlk0fJiPlhVTHVTOxHB/px2VCJTxyRz7KBe+Nv8fF1mj2avrKSpoIDmgkKaCvJpWb0G7K71N0FD\nhhA59QzqP8jFUVOz12v9k5KOdLkiXZYxhpLGkr1m3sqby93npEamMiZuDNOHTXe3CogKivJh1SLS\nkyjQiXSkpip4/VLY8jkccx1MvBNsnfOvndNpKNxezaLlxSxeWUx5fSuhgTYmjUhg6uhkThjamyB/\nrafyBWMM7Vu30pRfQFNBPs35BbQVFQFgBQYSPHoUvX7xC0KzMglJT8cW5foiGZqVtccaOgArOJj4\nX831xdsQ6fScxsmO+h3/DW+7A1xNq+sXI36WHwOjBnJM8jHu2be0mDTCA8N9XLmI9GSd85ulSHdQ\nugYWzoC6XXD2k5B+oa8r2osxhtW76li0fBfvrihmZ00zgf5+TEiLZ+qYZCYMiyckUCHuSDPt7bSs\nW0dTviu8NRUU4KisBMAWFUVIZibR504jJDOL4KNG7nft2w+7We5vl0uRnszhdFBUV+S6bXL3rNu6\nqnU0tDcA4O/nz5DoIZza71R3eBsSM4QQf/XPFJHORWvoRDrCusXw5lUQGAYXvAQpY31d0R6+L613\nh7gtFY34+1mcODSOqWOSmDg8gYhg9TU6khwNDTQvW/7f9W8rVmCaXTufBqSkEJqZSUhWJqFZWa6d\nJ/10u6vIoWh3trO5ZvMe4W199Xp3g+4gWxBpMWnu9W7DY4czOHowATb9LBQR39EaOhFfMAY+/zN8\nejckZ8L0lyAy2ddVAVBU0ci7K3axaHkx60vr8bPg2EG9mXXSQHJGJhIdqh0Oj5T20lKaCwrct1C2\nrlsPTif4+RE8bBjR557run0yI5OAhHhflyvSpbQ6WtlQvWGP8PZ99fe0O9sBCPUPZVjsMKYNmeZe\n7zYgagD+fvpKJCJdk356iXhLWyO8PRvWvA2jL4Cp81095XxoZ00zi3eHuJU7awEYmxrDvLNGcvpR\nScRFBPm0vp7AOJ20bdq0x/q39p07AbBCQghJH0Pva64hJDODkDHp2MLDfFyxSNfR1N7E+ur1e+w2\nualmEw7jACAyMJLhvYYzc/hMd3jrF9kPP0uz3CLSfSjQiXhDzTZXs/DS1TDpLjj2evBRX6Gy+hbe\nW1HMuyuKydtaDcCYvlHcOmU4PxuVRHK01n90JGdbGy2rVv13/VthIc5aV5i29e5NaGYmsZdc7Fr/\nNiwNK0C3dIkcjLq2OtZVrmNt1Vr37FtRbREG19KR2OBYRvQawUl9T3LdNtlrOMlhyerxJiLdngKd\niKe2fg2vXuxqT3DhazBk0hEvobqxjQ9Wl7Bo+S7+s7kSp4FhiRHcnJPGGaOT6N9Lsz4dxVFTQ1Nh\noesWyoJCWlauxLS1ARA4cCARkyYSmplFaFYmAf366culyEGoaqliXeU61lStcc++7WjY4X4+ITSB\n4b2Gc/qA0xkR6wpvcSFx+vslIj2SAp2IJ/L+Du/dBDGprmbhvYccsUvXtbTz4epSFq3YxZcbKrA7\nDQN7h3HdhCFMHZ3EkISII1ZLT2GMoX3nrv9uXlKQT+uGja4nAwIIGTGCmJkzd69/y8A/Nta3BYt0\ncsYYypvLWVu5do8+byWNJe5z+ob3ZXiv4UwbOo3hscMZFjuMXiG9fFi1iEjnokAncjgc7fDB72Dp\nszB4Ikx7DkKiO/yyTW12PllXxqLlu/h0fTltdid9okO48oSBnDE6iZHJkfoNtRcZh4PW9ev/u/6t\noBB7aSkAfuHhhGRkEDllCiGZmYSMGoVfiG5nFdkfYwy7Gne5wtuPNiypbHG15LCwSI1KJTM+073T\nZFpsmhp0i4gcgAKdyKFqrHQ1Cy/6wrVWbuKd4Ndxvdpa7Q4+W1/OohXFfLSmlOZ2B/ERQVw0vh9T\nxySTkRKtEOclzqYmmlesdG9e0rxsGc7GRgD8ExMJzc52tw8IGjwYy6YefdKzLd68mPkF8ylpLCEx\nLJE5mXOYMnAKTuNkW902d2j7Yfatrq0OAJtlY1D0II7vc7y7VUBaTBqhAaE+fkciIl2P+tCJHIqS\nVa5m4fWlcOajMGa6x0O+XbiTB3PXs6ummeToEG7OSWPK6CS+2ljBuyuKyV1dQn2LndiwQE4/KpGp\nY5IZmxqLzU8hzlP2igqaCgpoLiikqaCAljVrwG4HyyJoyBBXePth/Vty52g/IdJZLN68mDu+voMW\nR4v7mM2y0Te8LxUtFTS2u34ZEuAXwJCYIe5ZtxG9RjA4ejDB/sG+Kl1EpEs42D50CnQiB2vNO/DW\nLAiOdDUL75vl8ZBvF+7k92+upLnd4T5m87MIslk0tTuJCPbntJGJnDEmmWMH9SLApq22D5cxhrai\nInf/t+b8fNq2bgXACgwkZPRoQrJc4S0kPR1bZKSPKxbxjWZ7M9Ut1VS3VFPZUun+c1VLFVUtVVS3\nVlPVXMXaqrXu9gA/FuAXwLQh09w7TQ6KGqQG3SIih0GNxUW8xemEzx+Af98HfbJcYS4yyStDP5i7\nfo8wB+BwGozNj79dks2JQ3sT5K/b+g6HaWujZe1aV3grdIU4R1UVALboaEIyM4k+/zxCMjMJHjkS\nv0A1Vpfu6WADWnWr61izvXmf4wT6BRITHENscCyxwbH7DHMAdqedW46+pSPfkoiI/IgCnchPaW2A\nt6+Bte/AmBlwxiMQ4L3bhHbV7PuLU0u7g0kjErx2nZ7A0dBAc+Gy/65/W7EC0+K6FSygXz/CTzzR\nvf4tcMAArTuULqujAlpqVOoej2OCYogNiSU2KJaY4BjCAsL2+Hsz+Y3JFDcW7zVuYlhih713ERHZ\nmwKdyP5Ub3U1Cy9bA5PvgWOu9Wqz8G82VWJZsK+7ntX8+8DaS0rct082FRTQun69azbVz4/g4cOJ\nPv88QjOzCMnMICA+3tfliuxXZwloh2pO5py91tAF24KZkznnsMcUEZFDp0Ansi9FX8Jrl4DTDhe9\n7mpN4CXGGJ77cgv3vb+OXuGB1DXbabU73c+HBNi4OSfNa9frDozTSevGjXusf2vftQsAKzSU0PQx\nRMyeTWhmBsGjx2ALVyN18Z2uGtAO1ZSBUwD2uculiIgcOdoUReR/LX0W3v8txAzY3Sx8sNeGbmqz\n89t/rmTR8l3kjEzgz+eN4eO1ZXvtcnl2Rh+vXbMrcra20rJqlTu8NRUW4qzbvd15XG/3zpMhmVkE\nD0vD8tfvpqTjdFRAiwmO6VQBTUREOhdtiiJyqOxt8MFvIe95GDIZpj0Lwd5raFtU0cjVL+azoaye\n35yWxjUnDcKyLM7O6NPjA5y9uprmwmU0F+TTlF9Ay6pVmPZ2AAIHDSIyJ4eQzExX+4CUFH3JFY/0\nlBk0ERHpGRToRAAaK1y3WG79Co6bC6fe5tVm4R+vLWXuq8uw+Vn84/JxnDg0zmtjdzXGGNp37nTN\nvOUX0FSQT9vGTa4nAwIIGTmSmEsuJjQri5CMDPxjYnxbsHR6CmgiItKTKdCJlKyEVy6ExjI451kY\nfZ7XhnY6DfM/3sD8jzcwMjmSp2ZmkRIb6rXxuwJjt9Oyfj3N+QU0FRbQnF+AvawMAL+ICEIy0ok6\nYyqhWZkEjxqFX7CaDfd0CmgiIiIHT4FOerbVb7vaEgRHw+XvQ59Mrw1d29TOr15bxifrypiW2Zd7\nfn4UwQHdv6ecs6mJ5hUraMrf3T5g2TKcTU0A+CcnETpunHv9W9CQwVh+apbe3XVkQPthLdqP16XF\nBsUSGxJLqH+oApqIiHR7CnTSMzmd8Nn98NmfoO9YuGABRHivd9La4jpmLchnZ3Uzd501kplH9++2\nXyzt5eU0FRS6dqAsKKBlzRpwOMCyCBo6lKizzyIkM4vQzAwCkpN9XW6Ps3jzYq/vQqiAJiIi0nko\n0EnP01oPb82Cde9C+kVwxsPgH+S14f+1bCe/++dKIoL9efXqo8nqH+u1sX3NGEPbliL35iVNBfm0\nb90GgBUURMjo0fT65ZWu9W9jxmCLjPRxxT3b4s2L9+gTVtxYzB1f3wGwR6hTQBMREem61LZAepaq\nLa5m4eXrXM3Cj77Ga83C2x1O7n9/Hc99uYWxqTE8flEm8RFdez2YaWujZc0amgoKaSpw3ULpqK4G\nwBYdTUhWFqG7d58MHjECKzDQxxXLj01+YzLFjcV7HQ+yBTEkeshhbbOvgCYiInJkqG2ByP/a8rlr\nJ0tjYOY/YdAErw1dXt/KtS8X8N2WKi47NpVbpgwnwNb11oY56utpXrbsv+vfVqzAtLYCENC/H+En\nn7x7/VsmgQMG6Mt7J1TeVE5+aT55pXn7DHMArY5WooKiNIMmIiLSDSjQSfdnzH+bhfcaDDNegV6D\nvDZ8wbZqrlmQT21zOw9fMIafZ/T12tgdrb24mKYC186TTQUFtK5f7/rvZbMRPHw4MdMvcK9/84/r\nua0WOrOdDTvJL813hbiSPLbVu26BDfUPJcgWRKujda/XJIUl8dSkp450qSIiItIBFOike7O3wXs3\nQcELMPQ0OOdvEOyddV3GGF7+bht3vLOaxKhg3rzmOEYkd941Y8bppHXDxj3Wv9l3uWZw/EJDCUlP\nJ+K6a13r30aNwi8szMcVy/8yxrC1bit5pXnuEPfDLFxkYCSZCZmcn3Y+WQlZDIsdRm5R7h5r6ACC\nbcHMyZzjq7cgIiIiXqZAJ91XQzm8djFs+waOvxEm3Oq1ZuEt7Q5u+9cqXsvbwUlD45g/PZ3o0M61\nfszZ2krLypXu8NZcuAxnXR0A/nFxrvVvl11OSFYmwWlpWP76cdDZOI2TjTUb3eEtvzSfiuYKAGKD\nY8lKyOLSkZeSnZDNkJgh+Fl73ub7w8Yn3t7lUkRERDoPfYOT7ql4uatZeFMlTHsORp3rtaF3VDdx\nzYICVu6s5YYJg5kzcSg2P9+vM7JXV9NcWOhe/9ayejWmvR2AwMGDiMzJISQrk9CsLAL69tXaqE7I\n7rSzvno9eSWuGbiCsgJqW2sBSAhNYHzSeLISsshKyGJA5MGtYZwycIoCnIiISDemQCfdz6o34e3Z\nEBoLv/gAktO9NvSXGyq4/pUC7A7D3y7JZtKIBK+NfSiMMbTv2OEOb00FBbRt2uR6MiCAkKOOIvbS\nSwjJzCIkIx3/mBif1Ck/rd3RzurK1e5bKAvLCmlsbwQgJSKFU1JOITshm6yELPqE91EIFxERkb0o\n0En34XTCp/fAF3+GlPGuZuHh8V4Z2hjD059v5oEP1jE4PpynZmYxMC7cK2Mf1PXtdlrWrXevf2su\nKMBeXg6AX2QkoRkZRJ15pqt9wFFH4RfctdsldFct9hZWVqx0z8AtL1/uXt82MGogUwZMcc/AJYT5\n5pcFIiIi0rUo0En30FIHb10N69+DjIthyl+81iy8odXOza8v5/1VJUwZncQD00YTFuS9vzq1ixZR\n9vAj2IuL8U9KIv5Xc4mYMIHmFSt2h7d8mpYtxzQ1ARCQnEzo0Ue72wcEDR6M5df1WiT0BI3tjSwr\nW+ZuI7CyYiV2px0Li7TYNKYNnUZ2QjYZ8Rn0Cunl63JFRESkC1Jjcen6qjbDKzOgYgOcdh+Mu8pr\nzcI3lTdw9Yv5bC5v4PenD+fKE7zbe6120SKK/3gbpuW/uxC6azcGLIugtDRCMzNd698yMwlISvLa\n9cW7altrKSwrdM/Ara1ai8M4sFk2RvYa6Z59y0jIIDKw8+6IKiIiIr6nxuLSM2z+N7x2qSsEXfwm\nDDzZa0Pnri7h168tJ9DfjwVXjOfYwb29NvYPyh56eM8wB2AMfuHh9Hn4YULSx2CLiPD6dcU7Kpor\nKCgtcM/AbajegMEQ4BfAqN6juGLUFWQlZJEel05oQKivyxUREZFuSIFOuiZj4NunIfcP0HsozHgZ\nYgd6ZWiH0/DQh+t5/NNNjOkbxZMzs0iODvHK2D/W+M032IuL9/mcs7GR8BOO9/o1xTMljSV79IDb\nUrsFgBD/EMbEjWF2+myyErIYHTeaIJt3bvkVERER+SkKdNL12Fth8a+h8EVI+xmc8wwEeWcWq7qx\njRsWFvLFhgqmj03hjjNHEhzgnd51P2gvLaPsT3+i7r33wGYDh2Ovc/x1W6XPGWPYUb/DHeDySvPY\n2bATgPCAcDLiMzh78NlkJWQxInYEAbYAH1csIiIiPZFHgc6yrNOA+YANeNYYc///PD8LuBZwAA3A\nVcaYNZ5cU3q4+lJXs/Dt38KJN8PJfwAvbQiyamctsxbkU1bXyn3njGLGuH5eGfcHxm6n+uWXKZ//\nKKa9nd7XXYd/cjKl8+btcdulFRxM/K/mevXacmDGGLbUbiGvNM8d4sqaygCIDoomKyGLi4ZfRHZC\nNkNjhmLzUpN6EREREU8cdqCzLMsGPA5MAnYASy3Leud/AtvLxpindp9/JvAQcJoH9UpPtqsQFl4E\nTVVw3j9g5M+9NvQ/83fwh7dWEhsWyGuzjiE9JdprYwM0FRZScuc8WtetI+yEE0i89RYC+/cHwC/A\nf69dLqOmTvXq9WVvDqeDDTUbXLNvJXkUlBVQ1VIFQFxIHFkJWe4ecAOjB+JnaSdRERER6Xw8maEb\nB2w0xmwGsCxrIXAW4A50xpi6H50fBnS+LTWla1j5BvzrWgjtDVfkQtIYrwzbZndy9+I1/N83Wzl6\nYCx/vTCT3uHeW/tkr66m/KGHqHn9DfwTE+nz6HwiJk3aY6fMqKlTFeCOgHZnO2sr17rXvxWUFVDf\nVg9An/A+HN/neHeIS4lIURNvERER6RI8CXR9gO0/erwDGP+/J1mWdS1wIxAITNjfYJZlXQVcBdCv\nn3dvdZMuzOmAT+6CLx+GfsfA+S9CeJxXhi6ta2H2SwXkb63mlycM4LenDcPf5p1ZGON0Uvvmm5T9\n+S84GhqIveIXxM2ejV9YmFfGlwNrdbSyqmKVewZuWfkymu3NAKRGpjK5/2R3gEsK15pFERER6Zo6\nfFMUY8zjwOOWZV0I3Apcup/zngGeAVcfuo6uS7qAljr455WwIReyLoPTHwT/QK8MvbSoitkvFdDY\nauexGRlMHZPslXEBWtaupeTOeTQvW0ZIdhaJt91G8NChXhtf9q2pvYnl5cvdM3ArylfQ5mwDYEjM\nEM4adBbZia5bKHuHeL8FhYiIiIgveBLodgIpP3rcd/ex/VkIPOnB9aQnqdzkahZeuRF+9mcYe6VX\nmoUbY3jh6yLuXryWvjEhLLhiPGmJ3tkh09HQQPmjj1K94CVs0dEk3X8fUWedpVv3Okh9Wz2FZYXu\nHSjXVKzBbuz4WX4Mix3G9GHTyUrIIjM+k+hg766JFBEREeksPAl0S4EhlmUNwBXkpgMX/vgEy7KG\nGGM27H44BdiAyIFs+gRevwwsG1zyNgw40SvDNrc5uOWtlbxZuJOJw+P5y/npRIV4vtW8MYa6996j\n7P4/Ya+oIHr6BcTPnYstKsoLVcsPqluqKSgtcO9Aub56PU7jxN/Pn6N6HcWlIy8lOzGb9Lh0wgPD\nfV2uiIiIyBFx2IHOGGO3LOs6IBdX24LnjTGrLcuaB+QZY94BrrMsayLQDlSzn9stRQBXs/D/PAFL\nboW4YTDjFYhJ9crQ2yqbuHpBPutK6rhx0lCuO2Uwfn6ez5y1bt5CyV3zaPrmPwSPHEnfJx4nZNQo\nL1Qs5U3l7tm3/NJ8NtZsBCDIFsSYuDFcPfpqdxPvEH/vN34XERER6QosYzrfcrXs7GyTl5fn6zLk\nSLK3wru/gmUvwbAz4OdPQ5B3Zln+vb6MOQuXYYxh/vQMThkW7/GYzuZmKp5+msrnnscvOJi4X80l\n5oILsGzqTXa4djXscoe3/NJ8ttZtBSDUP5SM+Az3+reRvUYSaPPOWkoRERGRzsqyrHxjTPaBzuvw\nTVFEDqi+BF6dCTuWwkm/g5N+65Vm4U6n4fFPN/LQR9+TlhDB0xdn0b+X57tM1n/yKaX33EP7zp1E\nnXUW8TffhH9vbbJxKIwxbK3buscMXHFjMQCRgZFkJmRy3tDzyErIYljsMPz99KNKREREZF/0LUl8\na2c+LJwJLTVw/v/BiLO8MmxdSzu/fm05H64p5az0ZO4/ZzQhgZ7NnrXt2EnpvffS8MknBA4eRL//\ne4GwceO8Um935zRONtZsdM++5ZfmU9FcAUBscCxZCVlcNvIyshKyGBIzRE28RURERA6SAp34zorX\n4J3rISwerlgCid5Ze7ahtJ6rX8xna1UTt08dwWXHpnq006Rpa6Py7/+g4sknwbKIv/kmYi+5BCvA\n8w1Vuiu708766vXkl7hm4ArKCqhtrQUgITSB8Unj3T3gUiM9+/8jIiIi0pMp0MmR53TAx3fCV/Oh\n//Fw/gsQ5p1bFhevKObmN5YTGujPy1eOZ/zAXh6N1/if/1Ay7y7aNm8mYtIkEv7wewKS1IT6f7U7\n2lldudp9+2RhWSGN7Y0ApESkcErKKWQnuNbA9QnvowAnIiIi4iUKdHJktdTubha+BLKvgNP/BDbP\nZ7rsDicP5q7n6c83k9EvmicvyiIxKviwx2svK6PsgQepe/ddAlJSSHn6KcJPOsnjOruLFnsLKytW\nklfiCnDLy5fT4mgBYFDUIKYMmEJWQhZZCVkkhCX4uFoRERGR7kuBTo6cio3wynSo3gJTHoKxV3hl\n2MqGVq5/pZCvN1Uy8+h+3HbGSAL9D28NlrHbqX75FcoffRTT2krv2bPpddUv8Qs+/HDYHTS2N7Ks\nbJl7E5OVFSuxO+1YWKTFpnHu0HNdTbwTMokNjvV1uSIiIiI9hgKdHBkbPoI3fgE2f7jkX5B6vFeG\nXb69hmsW5FPR2MaD547mvOyUwx6redkyiu+cR+vatYQddxyJf7yVwNRUr9TZ1dS21lJYVuiegVtb\ntRaHcWCzbIzsNZKLh1/sauIdn05kYKSvyxURERHpsRTopGMZA9/8FT68DeJHwPSXIaa/V4Z+dek2\n/vj2auIigvjnrGMZ1TfqsMaxV1dT/tDD1Lz+Ov4JCfR55BEicib3qHVelc2V7t0n80rz2FC9AYMh\nwC+AUb1HccWoK8hKyCI9Lp3QgFBflysiIiIiuynQScdpb4FFc2DFQhh+Jpz9pFeahbfaHdzxzhpe\n+W4bxw/uzaMzMogNO/RG08bppPattyh78M846uuJvfxyel97LbZwz3vVdXYljSV79IDbUrsFgBD/\nEMbEjWF2+myyE7IZFTeKIFuQj6sVERERkf1RoJOOUVcMrxwnwA0AACAASURBVF7k6jN38h/gxJu9\n0iy8uLaZWQsKXLdanjyImyanYfM79Jm0lvXrKbnjTpoLCwnJzCTx9tsJThvqcX2dkTGGHfU73OEt\nrzSPnQ07AQgPCCczIZOzB59NVkIWI3qNIMBP7RhEREREugoFOvG+Hfmw8EJorYcLFsDwqV4Z9ptN\nlVz3cgEt7Q6empnJaUcdevsAR0MDFY/9laoFC7BFRpJ0771EnX0WlhfCZmdhjGFL7RbySvPcIa6s\nqQyA6KBoshKymDl8JlkJWQyNGYrNz7OG6yIiIiLiOwp04l3LF8I7N0BEAlz8ISSM9HhIYwzPfbmF\n+95fR2qvUJ6++GgGx0cc8hj1H3xA6X33Yy8vJ/qC84mfOxdbdLTH9fmaw+lgQ80G9xq4/NJ8qlqq\nAIgLiXP3f8tKyGJg9ED8rO4TXkVERER6OgU68Q6nw7XxyTd/hdQT4LwXIMyzpt4ATW12fvPGCt5d\nUUzOyAT+fN4YIoIP7ZbA1i1bKL3rLhq//obgESPo+9fHCBk92uPafKXd2c66ynXu2beCsgLq2+oB\n6BPeh+P7HO8OcSkRKT1qcxcRERGRnkaBTjzXXAP/vAI2fgRjfwmn3eeVZuFbKhqZ9WI+G8rq+c1p\naVxz0qBDCifOlhYqnn6aqmefwwoOJuGPtxIzfTqWrXPdYrh482LmF8ynpLGExLBE5mTOYcrAKe7n\n2xxtrKxY6Vr/VpLHsvJlNNubAUiNTGVy/8lkJWSRnZBNUvih34YqIiIiIl2XAp14pmLD7mbhRXDG\nI5B9uVeG/XhtKXNfXYbNz+KFX4zjhCFxh/T6+n//m9K776F9xw4iz5xKws034x93aGMcCYs3L+aO\nr++gxdECQHFjMXd8fQcbqjfg7+dPfmk+K8pX0OZsA2BIzBDOGnQW2YmuGbjeIb19Wb6IiIiI+JgC\nnRy+75e4ZuZsgXDpIuh/rMdDOp2G+R9vYP7HGxiZHMlTM7NIiT34vmftO3dSct99NHz0MYGDBtHv\nhRcIGz/O47o6yvyC+e4w94MWRwvPrXoOP8uP4bHDmT5sunsNXFTQ4fXaExEREZHuSYFODp0x8NV8\n+OgOSDzK1Sw8up/Hw9Y2tTP31UI+XV/OtMy+3PPzowgOOLjbI01bG5UvvEDFE08CEH/Tr4m95BKs\nwEPvT3ck2J12viv5juLG4v2e89X0rwgP9Lxvn4iIiIh0Xwp0cmjam127WK58DUacDWc/AYGeN+Je\nW1zH1S/mU1zbzF1nH8XM8f0Oer1c47ffUTJvHm2bNhExaSIJv/89AcnJHtfkbXannbzSPHKLcvl4\n68dUt1ZjYWEwe52bFJakMCciIiIiB6RAJwevbperv9yuQphwK5xwE3hhB8V/LdvJb/+5gsjgABZe\ndTRZ/WMP6nX28nJKH3iQukWLCOjbl75PPUnEySd7XI83OZwOCsoK+GDLB3y07SOqWqoI8Q/h5JST\nyUnNoa61jnu/vXeP2y6DbcHMyZzjw6pFREREpKtQoJODs30pvHoRtDW6brEcNuXArzmAdoeT+95b\nx/NfbWFcaix/vSiD+IjgA77OOBxUv7KQ8kcewbS20nv2NfS66ir8gg/82iPB4XRQWFZIblEuH279\nkMqWSkL8Qzip70nkpOZwfJ/jCfb/b62BtsCf3OVSRERERGR/FOjkwApfgnfnQmQyXPw2JIzweMiy\n+haue7mQ77ZUcdmxqdwyZTgBtgM3vG5evpySO+fRsmYNYcceS8IfbyVowACP6/GU0zhZXr6cD7Z8\nwIdbP6S8uZxgWzAn9D2BnNQcTuhzAqEB+97cZcrAKQpwIiIiInJYFOhk/xx2V7Pw/zwOA050NQsP\nPbjbIX9K/tZqZr+UT21zO49ckM7ZGX0OXEpNDWUPP0LNa6/hHxdHn0ceJiInx6dNs53GyYryFeQW\n5bJk6xLKmsoIsgVxQh9XiDux74n7DXEiIiIiIt6gQCf71lwNr18Omz+F8bNg8j1g8+zjYozhpW+3\nceei1SRGBfPmNccxIjnyp1/jdFL79r8oe/BBHHV1xF5yCb2vvw5buG82DDHGsLJipTvElTSWEOAX\nwPF9jufGrBs5OeVkwgI83yRGRERERORgKNDJ3srXu5qF12yHMx+DzEs8HrKl3cEf317F6/k7OGlo\nHPOnpxMd+tMtBVrWf0/JvHk05+cTkpFB4h23E5yW5nEth8oYw5rKNeQW5ZJblMuuxl34+/lzfPLx\n3JBxAyennExEYMQRr0tERERERIFO9rT+A/jnlRAQDJe9C/2O9njIHdVNXLOggJU7a7lhwmDmTByK\nzW//t0o6Ghqp+OtfqXrxRWwRESTdcw9RPz8by+/Aa+y8xRjD2qq17hC3s2En/pY/xyQfw+z02ZzS\n7xQiA396dlFEREREpKMp0ImLMfDlw/DxPEgaDRe8BNEpHg/75YYKrn+lALvD8Owl2UwckfATJRjq\nc3Mpvfc+7GVlRJ9/PnG/mot/TIzHdRwMYwzfV3/vDnHb6rfhb/kzPnk8V4++mgn9JhAVFHVEahER\nERERORgKdAJtTfDO9bDqDThqGpz5Vwj0bDMPYwxPfbaZB3PXMTg+nKdmZjEwbv/r3tqKiii5624a\nv/qKoBHD6fvofELS0z2q4WDr3FCzwbUmrmgJRXVF2Cwb4xLHccWoK5iQMoHo4OgOr0NERERE5HAo\n0PV0tTtczcKLV8Cpt8HxN3rcLLyh1c7Nry/n/VUlTBmdxAPTRhMWtO+PmrOlhcpn/kbl3/6GFRRE\nwi23EDNjOpZ/x340N9Vscs/Eba7djJ/lx9jEsVwy8hJO7XcqscGe7+YpIiIiItLRFOh6sm3fwqsz\nob0ZZrwCaad7POTGsgaufjGPLRWN3PKz4Vx5woD9thZo+OwzSu6+h/bt24mcOpX4m28iID7e4xr2\nZ3PtZvdM3MaajVhYZCdmc+GwC5nYfyK9Qnp12LVFRERERDqCAl1PVfAivPsriOoLly6C+GEeD/nB\nqhJuen05gf5+LLhiPMcO7r3P89p37aL0vvuo//AjAgcOpN8//kHY0eM9vv6+FNUWuWbituayoXoD\nFhaZCZn8YfwfmNR/Er1D9l2jiIiIiEhXoEDX0zjssOQW+PYpGHgynPt3j5uFO5yGvyxZzxP/3sSY\nvlE8OTOL5OiQvc4zbW1U/d//Uf74E2AMcTfeSK/LLsUK/On2BYdqW902lmxdQm5RLuuq1gGQGZ/J\n78b9jkn9JxEf2nGzgCIiIiIiR5ICXU/SVAVvXA6b/w1Hz4ZJd3ncLLy6sY0bFhbyxYYKZoxL4fap\nIwkOsO11XuN331Eybx5tGzcRfuqpJPz+9wT27ePRtX9se/12lhS5QtzaqrUAjIkbw2/G/oZJ/SeR\nGJbotWuJiIiIiHQWCnQ9RdlaeGUG1O2Esx6HjJkeD7lqZy2zFuRTVtfKfeeMYsa4fnudY6+ooOzB\nB6n91zsE9OlD3yeeIGLCKR5fG2BXwy53iFtVuQqA0b1Hc1P2TUzuP5mk8CSvXEdEREREpLNSoOsJ\n1r0Hb/4SAkLhssWQMs7jId/I38Etb60kNiyQ12YdQ3rKnlv7G4eD6ldfpfzhR3C2tNBr1tX0vvpq\n/EL2vhXzUJQ0lrg3NllRsQKAkb1GcmPWjUxOnUyfcO/N+omIiIiIdHYKdN2ZMfDFn+GTeyBpDEx/\nGaI8Czxtdid3L17D/32zlWMG9uKxCzPoHR60xznNK1dScsedtKxeTegxR5P4x9sIGjjgsK9Z2ljK\nh1s/JLcol2XlywAYHjucuZlzmZw6mZQIzxugi4iIiIh0RQp03VVbE/zrWlj9Jow6D858DAI8mx0r\nrWth9ksF5G+t5qoTB/KbnDT8bX7u5x21tZQ9/DA1r76Gf+/e9HnoL0Scfvp+2xb8lLKmMj7c+iFL\nipZQUFYAQFpMGjdk3EBOag79Ive+vVNEREREpKdRoOuOara7moWXrISJd8JxczxuFv7dliqufbmA\nxlY7j83IYOqYZPdzxhhq3/4XZQ8+iKOmhthLLqb39ddjCw8/pGtUNFe4Z+IKSgswGIbEDOG69OuY\nnDqZAVGHP8snIiIiItIdKdB1N1u/cTULd7TBha/C0ByPhjPG8MLXRdy9eC0psaEsuGI8aYkR7udb\nvv+eknnzaM7LJyQ9ncTnniV4+PCDHr+yuZKPt31MblEueaV5OI2TQVGDuCb9GnL65zAweqBH9YuI\niIiIdGcKdN1J/j9g8U0Q3Q9mvAJxaR4N19zm4A9vreStwp1MHB7PX85PJyokAABnYyPljz9B1Qsv\nYAsPJ+nuu4g65xwsP78DjArVLdV8vO1jPij6gKUlS3EaJ6mRqVw1+ipy+ucwOGawR3WLiIiIiPQU\nCnTdgaMdcv8A3z0Dg06Fc5+DkBiPhtxW2cTVC/JZV1LHjZOGct0pg/HzszDGUL/kQ0rvvRd7aSnR\n551L3I034h/z09eraanhk+2fkFuUy7fF3+IwDvpH9ufKUVeSk5rDkOghh7XWTkRERESkJ/Mo0FmW\ndRowH7ABzxpj7v+f528ErgTsQDnwC2PMVk+uKf+jqQpeuwSKvoBjrnOtmfOwWfin68uYu3AZxhie\nv2wsp6TFA9C2dSsld91N45dfEjRsGH0eeZjQjIz9jlPbWssn2z4hd2su3+76FruxkxKRwuVHXU5O\nag5pMWkKcSIiIiIiHjjsb/6WZdmAx4FJwA5gqWVZ7xhj1vzotEIg2xjTZFnWNcADwAWeFCw/Urra\n1Sy8vhjOfgrSZ3g0nNNpePzTjTz00fekJUTw9MVZ9O8VhrO1lcpn/kbl3/6GFRBAwh/+QMyFM7D8\n9/741LfV8+n2T8ktyuXrXV9jd9rpE96HS0ZeQk5qDsNjhyvEiYiIiIh4iSdTOeOAjcaYzQCWZS0E\nzgLcgc4Y8+mPzv8PMNOD68mPrX0X3rwKgiLg8vehb7ZHw9W1tHPjq8v5aG0pZ6Unc/85owkJtNHw\nxReU3HU37du2ETllCvG//Q0B8fF7vLahrYFPt3/KkqIlfLXrK9qd7SSFJTFz+ExyUnMY2WukQpyI\niIiISAfwJND1Abb/6PEOYPxPnH8F8P7+nrQs6yrgKoB+/dRjbL+Mgc8fhE/vgeRMV7PwyCSPhvy+\ntJ6rX8xne1UTt08dwWXHpmIvKWHHffdTv2QJgQMG0O/vzxN2zDHu1zS2N/LZ9s/ILcrly51f0uZs\nIyE0gRnDZpCTmsOo3qMU4kREREREOtgR2RTFsqyZQDZw0v7OMcY8AzwDkJ2dbY5EXV1OWyO8fQ2s\n+ReMvgCmzve4WfjiFcXc/MZyQgP9efmXRzO2bwRVz/+d8scfB6eTuLlzif3F5fgFBtLU3sTnOz4n\ntyiXL3Z+QaujlfiQeM5PO5+c1BxGx43GzzrwLpciIiIiIuIdngS6nUDKjx733X1sD5ZlTQRuAU4y\nxrR6cL2erWYbvHIhlK2GSXfBsdd71Czc7nDyQO56nvl8M5n9onlyZhYR61ey5YZ5tG7YSPgpp5Bw\nyy04EmP5aMe/yS3K5fMdn9PiaKF3SG+mDZlGTmoO6fHpCnEiIiIiIj7iSaBbCgyxLGsAriA3Hbjw\nxydYlpUBPA2cZowp8+BaPVvRV/DaxeCww4Wvw5CJHg1X2dDKdS8X8s3mSi4+uj+/PyaBmnvuYOu/\n/kVAcjLxjz1M4VB/Ht30CJ999hnN9mZig2M5a/BZnJZ6GhnxGdj8bF56cyIiIiIicrgOO9AZY+yW\nZV0H5OJqW/C8MWa1ZVnzgDxjzDvAg0A48Pru9VTbjDFneqHuniPveXjvZogZ4GoW3nuIR8Mt317D\nNQvyqWhs48FzRjJx0zdsn3oFzqYm6mdM5q1jbXxSdjtNu5qIDY5l6sCp5KTmkJWQpRAnIiIiItLJ\nWMZ0vuVq2dnZJi8vz9dl+JajHd7/LeQ9B4MnwrTnICTaoyFfXbqNP769mriIIJ4eF0ro43+mdfVq\niof15tEJLWyKaiE6KJqJ/SeSk5pDdkI2/n7qPS8iIiIicqRZlpVvjDngVvb6tt4ZNVbAa5fC1i/h\n2Btg4h3gwexYq93BHe+s4ZXvtjGhbwBXbVmI37VfUBpm8cKZfqxMdzAx9Wf8pn8OY5PGEuAX4LW3\nIiIiIiIiHUeBrrMpWeVqFt5QCj9/BsZ41od9V00zs15ayurKpVxufUPO39cQ0WT4eGwQ1RefxswR\nZzA+abxCnIiIiIhIF6RA15mseQfemgXBkbubhWcd9lDtznZeKPiYx759g6TGZTzwRSMjtkP5wFhq\nb76GWSdeQIBNIU5EREREpCtToOsMnE747E/w2f3QJxumvwQRiYc8jN1pJ680jw+2fMDiTUswLXVc\n+K0fU/LsWGFhxN15E8POOx/LT20GRERERES6AwU6X2ttgLdnwdpFMOZCOONhCAg+6Jc7nA7yS/PJ\nLcrlo20fUdVShZ8JJKMwkVlftxNV30DUudOI//Wv8Y+J6cA3IiIiIiIiR5oCnS9VF7mahZevhZx7\n4ejZB9Us3OF0UFhWyAdFH/DR1o+obKkkxD+E7LjjKFkay9lL8hhb+j1BaWkk3n47oZkZHf9eRERE\nRETkiFOg85UtX8Brl4BxwEWvu1oT/ASncbKsbBm5Rbl8uPVDypvLCbYFc2LfE8lJzcFZNYD/3PsE\nV695Df+gABJ+/ztiLroIy1//i0VEREREuit92/eFpc+6eszFDoQZC6HXoH2e5jROVpSvILcolyVF\nSyhrLiPIFsQJfU4gJzWHE/ueSLAthAV/fZXkF+7igsYK/E+dTOptfyAgIeEIvykRERERETnSFOiO\nJHsbvP8byP87DMmBaX+D4Kg9TjHGsLJipSvEbV1CSWMJgX6BHN/neHJSczgp5STCAsIAqNqyndy5\ntzB2/VJqeiWR+PDfiDnxeF+8MxERERER8QEFuiOlodx1i+W2r+G4uXDqbe5m4cYYVleuds/E7Wrc\nRYBfAMclH8cNGTdwSsophAeGu4cy7e2se+JZWv72NAOcDnaecymn3P4rbEFBvnp3IiIiIiLiAwp0\nR0LxClh4ITSWwznPwujzMMawtnINuUW55BblsrNhJ/5+/hybfCzXZlzLySknExkYuddQTfn5fP+7\nWwnaXsS6PiNJu/cOJo4/ygdvSkREREREfE2BrqOtfgveng3B0ZjL3mN9aBi5BfPJLcple/12/C1/\nxiePZ9aYWZyScgpRQVH7HMZeVUXJAw9S//bb1IRE8/HU67nuj5eTEBlyhN+QiIiIiIh0Fgp0HcXp\nhH/fh/n8ATb0zSD3qNNYsvQOiuqKsFk2xieN58pRVzIhZQLRwdH7HcY4HNS8/galDz2EvaGRN4ZM\nwP+yX3Db2RkE2NQgXERERESkJ1Og6wit9Wx841Jyy/PIHZTGFmclft+/wtjEsVw68lJO7XcqMcEH\nbvLdvGo1JfPm0bJiBWsThvDEMT/nussmc3ZGnyPwJkREREREpLNToPOizbWbyV2zkNy1C9lkM/jF\nRJMdfxQzU0/j1H6n0iuk10GN46iro/yR+VQvXEh7eBTzx17EhqOO5amLsxmRvPe6OhERERER6ZkU\n6DxUVFvk2thkay4bqjdgGUOm3cEtQ2cwMWs2vUN6H/RYxhjqFi2i9E8P4KiuZvW4ydweexxjR/Vj\n0QUZRIUGdOA7ERERERGRrkaB7iAs3ryY+QXzKWksITEskYuGX0Sbo43colzWV68HIDMkid9V1jAp\nOJH4C/bfLHx/WjdupOTOeTQtXYrfiJE8cuoscttiuOHUIcw9dQh+flZHvDUREREREenCLGOMr2vY\nS3Z2tsnLy/N1GYArzN3x9R20OFr2ei49Lp2cfqcyceNXJC57DYaeDuc8A8EHf1uks6mJiiefpPLv\n/8AvLIzamb9kVlUf2p0WD1+QzsQRCd58OyIiIiIi0gVYlpVvjMk+0HmaoTuA+QXz9xnm4kPjefHE\nv8CrF8P2/8AJv4ZTbgW/g9t50hhDw8cfU3LPvdiLi4k65+csPmYa931dwuD4EJ6+OJsBvcO8/XZE\nRERERKQbUaA7gJLGkn0eL28qg2dOgaZKOPd5OGraQY/Ztn07JXffTeNnnxM0dCix9/2JWzb48cFX\nJUwZncQD00YTFqT/NSIiIiIi8tOUGg4gMSyR4sbivY/bHYAFv/gAktMPaixnWxuVzz5L5dPPYNls\nxP/2t1TlnM35ryyjqLKJW342nCtPGIBlab2ciIiIiIgcmDpTH8CczDkEW3vuLhnsdDLH6gVXfXrQ\nYa7hq6/YMvVMKh59jPAJpzDw/fdYOvY0fv70t9Q0tfPiFeP45YkDFeZEREREROSgaYbuAKY0NEJF\nJfMjQynxt5FodzCnpo4pE2+B8PgDvr69tJTS+++n/v0PCOjfj5RnnyXk2GP5y5L1PPHvTYzpG8WT\nM7NIjg45Au9GRERERES6EwW6A/l4HlPqaphSV7Pn8U/vhfQL9/syY7dTtWABFY8+hnE46H3D9fS6\n4gpq7RbX/P07vthQwYxxKdw+dSTBAbYOfhMiIiIiItIdKdAdSO2OQzsONBUUUHLnPFrXryfspBNJ\nvPVWAlNSWLWzlqtfzKe8vpX7zxnF9HH9OqhoERERERHpCRToDiSqL9Ru3/fx/2GvqqLsz3+h9s03\n8U9Kos9jjxIxcSKWZfFG/g5ueWslsWGBvDbrGNJToo9A8SIiIiIi0p0p0B3IqbfBohugvfm/xwJC\nXMd3M04nNa+/QdlDD+FsbKTXL6+k9zXX4BcaSpvdyV3vrubF/2zlmIG9eOzCDHqHB/ngjYiIiIiI\nSHejQHcgo893/fvjea7bLKP6usLc7uPNq1dTMm8eLctXEDp2LIm330bQ4MEAlNa1cM2CfAq21XDV\niQP5TU4a/jZtLCoiIiIiIt6hQHcwRp//32C3m6O+nvL5j1L98svYYmJIfuBPRE6d6m478N2WKma/\nVEBTm52/XpjBGaOTfVG5iIiIiIh0Ywp0B6F20SLKHn4Ee3Ex/kmJhJ90EvUffoSjspKYGTOImzsH\nW2QkAMYY/vF1EfcsXktKbCgv/3I8QxMifPwORERERESkO1KgO4DaRYso/uNtmJYWAOy7iql5ZSH+\nffuS+vrrhBw10n1uc5uD37+5greX7WLi8AQeumAMkcEB+xtaRERERETEIwp0B1D28CPuMLcHh2OP\nMLetsomrF+SzrqSOX08ayrWnDMbPzzqClYqIiIiISE+jQHcA9uLifR8vKXH/+dP1Zcx5pRCA5y8b\nyylp8UekNhERERER6dkU6A7APykJ+65d+zzudBoe/3QjD330PWkJETx9cRb9e4X5oEoREREREemJ\ntIf+AcT/ai5WcPAex6zgYMKvu56rXszjLx9+z1ljknlr9nEKcyIiIiIickRphu4AoqZOBfjRLpdJ\nOH4xiws3R7O9qpw7po7g0mNT3e0KREREREREjhQFuoPwad9MHpx8C7tqmokODaBhrZ2oEDsv//Jo\nxg2I9XV5IiIiIiLSQynQHcDbhTv5/ZsraW53AFDd1I5lwfUTBinMiYiIiIiIT2kN3QE8mLveHeZ+\nYAw88/kWH1UkIiIiIiLiokB3ALtqmg/puIiIiIiIyJHiUaCzLOs0y7LWW5a10bKs3+3j+RMtyyqw\nLMtuWda5nlzLV5KjQw7puIiIiIiIyJFy2IHOsiwb8DhwOjACmGFZ1oj/OW0bcBnw8uFex9duzkkj\nJMC2x7GQABs356T5qCIREREREREXTzZFGQdsNMZsBrAsayFwFrDmhxOMMUW7n3N6cB2fOjujD+Ba\nS7erppnk6BBuzklzHxcREREREfEVTwJdH2D7jx7vAMYf7mCWZV0FXAXQr18/D8ryvrMz+ijAiYiI\niIhIp9NpNkUxxjxjjMk2xmTHxcX5uhwREREREZFOz5NAtxNI+dHjvruPiYiIiIiIyBHgSaBbCgyx\nLGuAZVmBwHTgHe+UJSIiIiIiIgdy2IHOGGMHrgNygbXAa8aY1ZZlzbMs60wAy7LGWpa1AzgPeNqy\nrNXeKFpEREREREQ82xQFY8x7wHv/c+y2H/15Ka5bMUVERERERMTLOs2mKCIiIiIiInJoLGOMr2vY\ni2VZ5cBWX9exD72BCl8XId2WPl/SkfT5ko6kz5d0JH2+pKN11s9Yf2PMAbf/75SBrrOyLCvPGJPt\n6zqke9LnSzqSPl/SkfT5ko6kz5d0tK7+GdMtlyIiIiIiIl2UAp2IiIiIiEgXpUB3aJ7xdQHSrenz\nJR1Jny/pSPp8SUfS50s6Wpf+jGkNnYiIiIiISBelGToREREREZEuSoFORERERESki1KgOwiWZZ1m\nWdZ6y7I2Wpb1O1/XI92LZVnPW5ZVZlnWKl/XIt2PZVkplmV9alnWGsuyVluWNcfXNUn3YVlWsGVZ\n31mWtXz35+tOX9ck3Y9lWTbLsgoty3rX17VI92JZVpFlWSsty1pmWVaer+s5XFpDdwCWZdmA74FJ\nwA5gKTDDGLPGp4VJt2FZ1olAA/B/xpijfF2PdC+WZSUBScaYAsuyIoB84Gz9DBNvsCzLAsKMMQ2W\nZQUAXwJzjDH/8XFp0o1YlnUjkA1EGmPO8HU90n1YllUEZBtjOmNT8YOmGboDGwdsNMZsNsa0AQuB\ns3xck3QjxpjPgSpf1yHdkzGm2BhTsPvP9cBaoI9vq5Luwrg07H4YsPsf/aZYvMayrL7AFOBZX9ci\n0lkp0B1YH2D7jx7vQF+GRKQLsiwrFcgAvvVtJdKd7L4dbhlQBnxojNHnS7zpEeA3gNPXhUi3ZIAl\nlmXlW5Z1la+LOVwKdCIiPYBlWeHAP4G5xpg6X9cj3YcxxmGMSQf6AuMsy9Kt4+IVlmWdAZQZY/J9\nXYt0W8cbYzKB04Frdy+D6XIU6A5sJ5Dyo8d9dx8TEekSdq9t+ifwkjHmTV/XI92TMaYG+BQ4zde1\nSLdxHHDm7nVOC4EJlmUt8G1J0p0YY3bu/ncZ8Bau31OwXQAAIABJREFUpVZdjgLdgS0FhliWNcCy\nrEBgOvCOj2sSETkouzeteA5Ya4x5yNf1SPdiWVacZVnRu/8cgmsDsXW+rUq6C2PM740xfY0xqbi+\nf31ijJnp47Kkm7AsK2z3ZmFYlhUGTAa65I7jCnQHYIyxA9cBubg2E3jNGLPat1VJd2JZ1ivAN0Ca\nZVk7LMu6wtc1SbdyHHAxrt9sL9v9z898XZR0G0nAp5ZlrcD1C9APjTHaWl5EuoIE4EvLspbD/7N3\n33Fy1fX+x1/faTtlazbbSza97qZCQEACqARD0ysiiHJBrz+9KkVFUTEEr+16C9ju9aqAiHAFwYuG\niBVRucKFtN30toRke7LZ2TYzO+37++OcmZ2yLcn2fJ6PxzzmzCkzZ3azk3mf7/f7+fIasEVr/ZsJ\nPqczItMWCCGEEEIIIcQUJS10QgghhBBCCDFFSaATQgghhBBCiClKAp0QQgghhBBCTFES6IQQQggh\nhBBiipJAJ4QQQgghhBBTlAQ6IYQQ05ZSKpIwXcNOpdS9o/jcVUqpKTlnkRBCiOnDNtEnIIQQQowh\nv9Z6xUSfhBBCCDFWpIVOCCHEOUcpdVQp9U2l1C6l1GtKqXnm+iql1ItKqTql1B+VUpXm+iKl1P8o\npWrN21vMp7IqpX6olNqjlPqdUso1YW9KCCHEOUkCnRBCiOnMldLl8saEbZ1a62rgu8BD5rrvAI9p\nrWuAJ4Bvm+u/DfxZa70cWAXsMdfPB76ntV4KeIG/G+P3I4QQQiRRWuuJPgchhBBiTCilerTWmQOs\nPwpcrrWuV0rZgRatdb5S6iRQorUOmeubtdYzlVIngHKtdV/Cc1QBv9dazzcffw6wa62/MvbvTAgh\nhDBIC50QQohzlR5k+XT0JSxHkLHpQgghxpkEOiGEEOeqGxPuXzGX/wa8z1x+P/BXc/mPwMcAlFJW\npVTOeJ2kEEIIMRS5kiiEEGI6cymldiY8/o3WOjZ1QZ5Sqg6jle0mc90ngUeVUvcAJ4DbzPV3Aj9Q\nSn0IoyXuY0DzmJ+9EEIIMQwZQyeEEOKcY46hW6O1PjnR5yKEEEKcDelyKYQQQgghhBBTlLTQCSGE\nEEIIIcQUJS10QgghxoU5abdWStnMxy8opW4dyb5n8FpfUEr96GzOVwghhJgKJNAJIYQYEaXUb5RS\nXx5g/XVKqZbTDV9a66u01o+NwnmtU0o1pDz317TWHz7b5xZCCCEmOwl0QgghRuox4BallEpZ/wHg\nCa11eALO6Zxypi2WQgghpi8JdEIIIUbqOSAfuCS2QimVB1wN/MR8vEEptUMp1aWUOq6U2jTYkyml\nXlJKfdhctiql/lUpdVIpVQ9sSNn3NqXUPqVUt1KqXin1/8z1HuAFoFQp1WPeSpVSm5RSP004/lql\n1B6llNd83cUJ244qpT6jlKpTSnUqpZ5SSjkHOee5SqkXlVLt5rk+oZTKTdheoZT6hVLqhLnPdxO2\n/UPCe9irlFplrtdKqXkJ+/1YKfUVc3mdUqpBKfU5pVQLxpQKeUqp583X6DCXyxOOn6GUelQp1WRu\nf85cv1spdU3CfnbzPawc7HckhBBi8pNAJ4QQYkS01n7gaeCDCavfC+zXWteaj3vN7bkYoexjSqnr\nR/D0/4ARDFcCa4D3pGxvM7dnY8wN96BSapXWuhe4CmjSWmeat6bEA5VSC4D/Bu4CCoBfA5uVUo6U\n97EemA3UAH8/yHkq4OtAKbAYqAA2ma9jBZ4H3gSqgDLgZ+a2G8z9Pmi+h2uB9hH8XACKgRnALOAj\nGP93P2o+rgT8wHcT9n8ccANLgULgQXP9T4BbEvZ7J9Cstd4xwvMQQggxCUmgE0IIcToeA96T0IL1\nQXMdAFrrl7TWu7TWUa11HUaQunQEz/te4CGt9XGt9SmM0BSntd6itT6iDX8GfkdCS+EwbgS2aK1/\nr7UOAf8KuIC3JOzzba11k/nam4EVAz2R1vqw+Tx9WusTwL8nvL/zMYLePVrrXq11QGv9srntw8A3\ntdavm+/hsNb6zRGefxS433xNv9a6XWv9rNbap7XuBr4aOwelVAlGwP2o1rpDax0yf14APwXeqZTK\nNh9/ACP8CSGEmMIk0AkhhBgxM6CcBK5XSs3FCDFPxrYrpdYqpf5kdgfsBD4KzBzBU5cCxxMeJ4Ud\npdRVSqlXlVKnlFJejNalkTxv7Lnjz6e1jpqvVZawT0vCsg/IHOiJlFJFSqmfKaUalVJdGCEpdh4V\nwJuDjCWsAI6M8HxTndBaBxLOwa2U+i+l1JvmOfwFyDVbCCuAU1rrjtQnMVsu/xf4O7Ob6FXAE2d4\nTkIIISYJCXRCCCFO108wWuZuAX6rtW5N2PYk8CugQmudA3wfo5vicJoxwkhMZWxBKZUBPIvRslak\ntc7F6DYZe97hJlRtwuieGHs+Zb5W4wjOK9XXzNer1lpnY/wMYudxHKgcpHDJcWDuIM/pw+giGVOc\nsj31/X0aWAisNc/hreZ6Zb7OjMRxfSkeM8/5BuAVrfWZ/AyEEEJMIhLohBBCnK6fAG/DGPeWOu1A\nFkYLUUApdT5w8wif82ngDqVUuVlo5d6EbQ4gAzgBhJVSVwHvSNjeCuQrpXKGeO4NSqkrlFJ2jEDU\nB/xthOeWKAvoATqVUmXAPQnbXsMIpt9QSnmUUk6l1EXmth8Bn1FKrVaGeUqpWMjcCdxsFoZZz/Bd\nVLMwxs15lVIzgPtjG7TWzRhFYv7DLJ5iV0q9NeHY54BVwJ2YhWyEEEJMbRLohBBCnBat9VGMMOTB\naI1L9I/Al5VS3cBGjDA1Ej8EfgvUAtuBXyS8Xjdwh/lcHRgh8VcJ2/djjNWrN6tYlqac7wGMVqnv\nYHQXvQa4RmsdHOG5JXoAIxB1AltSzjNiPvc84BjQgDF+D631zzHGuj0JdGMEqxnmoXeax3mB95vb\nhvIQxhjAk8CrwG9Stn8ACAH7MYrJ3JVwjn6M1s7ZiecuhBBi6lJaD9dTRQghhBDThVJqI7BAa33L\nsDsLIYSY9GSCUiGEEOIcYXbR/BBGK54QQohpQLpcCiGEEOcApdQ/YBRNeUFr/ZeJPh8hhBCjQ7pc\nCiGEEEIIIcQUJS10QgghhBBCCDFFTcoxdDNnztRVVVUTfRpCCCGEEEIIMSG2bdt2UmtdMNx+kzLQ\nVVVVsXXr1ok+DSGEEEIIIYSYEEqpN0eyn3S5FEIIIYQQQogpSgKdEEIIIYQQQkxREuiEEEIIIYQQ\nYoqalGPohBDTRygUoqGhgUAgMNGnIoQQ05bT6aS8vBy73T7RpyKEGGcS6IQQY6qhoYGsrCyqqqpQ\nSk306QghxLSjtaa9vZ2GhgZmz5490acjhBhn0uVSCDGmAoEA+fn5EuaEEGKMKKXIz8+XnhBCnKMk\n0AkhxpyEOSGEGFvT8nO27ml4cBlsyjXu656e6DMSYlKSLpdCCCGEEGJyqXsaNt8BIb/xuPO48Rig\n5r0Td15CTEIS6IQQk8pzOxr5l98eoMnrpzTXxT1XLuT6lWUTfVrTS93T8McvQ2cD5JTDFRvH5QvS\nj3/8Y7Zu3cp3v/vdMX+t0balfgvf2v4tWnpbKPYUc+eqO9kwZ8OEnEtVVRVbt25l5syZ4/7anZs3\n0/bgQ4Sbm7GVlFB4913kXHPNuJ+HmGa0Bn+HEdo6G4zbi//UH+ZiQn54/m7wvgmZxZBZBJmFkFUM\n7plgla+14twk//KFEJPGczsa+fwvduEPRQBo9Pr5/C92AYxaqNNao7XGYhm7HueRSASr1Tpmz39W\n5Kr3adtSv4VNf9tEIGKMT2rubWbT3zYBTFiomwidmzfT/KWNaHOcVripieYvbQSYkFA3kcH2TO3c\nuZOmpibe+c53TvSpjK9wELqb+sNa53HwJoS3zgYI9Y7suYI98OJX0tcrixHqMosgq8gMe0XJoS/2\nOCNzdN+fEBNMAp0QYtw8sHkPe5u6Bt2+45iXYCSatM4fivDZZ+r479eODXjMktJs7r9m6ZCve/To\nUa688krWrl3Ltm3b2Lt3L5/5zGf49a9/TUlJCV/72tf47Gc/y7Fjx3jooYe49tpr2bNnD7fddhvB\nYJBoNMqzzz6L3W5n/fr1rF69mu3bt7N06VJ+8pOf4Ha7qaqq4sYbb+T3v/89n/3sZ1m0aBEf/ehH\n8fl8zJ07l0ceeYS8vDzWrVvH8uXL+fOf/0w4HOaRRx7h/PPPP/0f5mBeuBdadg2+veF1iPQlrwv5\n4ZefgG2PDXxMcTVc9Y1hX/r666/n+PHjBAIB7rzzTj7ykY/w6KOP8vWvf53c3FyWL19ORkYGAJs3\nb+YrX/kKwWCQ/Px8nnjiCYqKiti0aRNvvPEG9fX1HDt2jAcffJBXX32VF154gbKyMjZv3jzqZdn/\n+bV/Zv+p/YNurztRRzAaTFoXiATY+L8beebgMwMes2jGIj53/ucGfc7e3l7e+9730tDQQCQS4Utf\n+hJZWVl86lOfwuPxcNFFF1FfX8/zzz9Pe3s7N910E42NjVx44YVorc/sjQ6j5Wtfo2/f4D8Hf20t\nOpj8c9CBAM1fvA/v0z8f8JiMxYso/sIXRvU8p7KdO3eydevW6RXotIaAtz+YeY8nt7R1NkB3M5Dy\n79ZTYPQQKFgA864wlnMq+u9/eJnxPKlyKuATW6GnFXrazPsWY7nbvO9pgbZ9xrZoOP057J700JcW\nAovAMxMsk/TinBAJJNAJISaN1DA33PrTcejQIR577DEuuOAClFJcfvnl/Mu//Avvete7uO+++/j9\n73/P3r17ufXWW7n22mv5/ve/z5133sn73/9+gsEgkUiE1tZWDhw4wMMPP8xFF13E7bffzn/8x3/w\nmc98BoD8/Hy2b98OQE1NDd/5zne49NJL2bhxIw888AAPPfQQAD6fj507d/KXv/yF22+/nd27d5/1\n+xux1DA33PrT8MgjjzBjxgz8fj/nnXceGzZs4P7772fbtm3k5ORw2WWXsXLlSgAuvvhiXn31VZRS\n/OhHP+Kb3/wm//Zv/wbAkSNH+NOf/sTevXu58MILefbZZ/nmN7/Ju971LrZs2cL1119/1ud6OlLD\n3HDrR+I3v/kNpaWlbNmyBYDOzk6WLVvGX/7yF2bPns1NN90U3/eBBx7g4osvZuPGjWzZsoWHH374\njF/3bKSGueHWj8RYBdujR4+yfv16LrjgAv72t79x3nnncdttt3H//ffT1tbGE088wfnnn8+pU6e4\n/fbbqa+vx+1284Mf/ICampoRX1jYtm0bn/rUp+jp6WHmzJn8+Mc/pqSkhHXr1rF27Vr+9Kc/4fV6\nefjhh1m7di0bN27E7/fz8ssv8/nPf559+/aRmZkZ/wxZtmwZzz//PMCIzn9cREJGIIu3qB1Pbmnr\nbDBazRJZHWYwK4e5lyUEtVhoKwO7a+jXvWJjcm8CMI65YiPYnZA3y7gNJRo1unL2tPQHwMTQ19MG\nrXvgyJ+grzP9eGUxgudQoS+2zuEZ2c9TiDEggU4IMW6Ga0m76Bsv0uj1p60vy3Xx1P+78Kxee9as\nWVxwwQUAOBwO1q9fD0B1dTUZGRnY7Xaqq6s5evQoABdeeCFf/epXaWho4N3vfjfz588HoKKigosu\nugiAW265hW9/+9vxL2M33ngjYHw593q9XHrppQDceuut3HDDDfFziX1Zf+tb30pXVxder5fc3Nyz\nen9xw7WkPbhs8Kvet205q5f+9re/zf/8z/8AcPz4cR5//HHWrVtHQUEBYPx8Dh48CBjzE9544400\nNzcTDAaT5s666qqr4r+PSCSS9LuK/X5G01AtaQDveOYdNPc2p60v8ZTw6PpHz+g1q6ur+fSnP83n\nPvc5rr76arKyspgzZ07853DTTTfxgx/8AIC//OUv/OIXvwBgw4YN5OXlndFrDme4lrRDl19BuKkp\nbb2ttJRZj//kjF5zLIPt4cOH+fnPf84jjzzCeeedx5NPPsnLL7/Mr371K772ta/x3HPPcf/997Ny\n5Uqee+45XnzxRT74wQ+yc+dOYPgLCxs2bOCTn/wkv/zlLykoKOCpp57ii1/8Io888ggA4XCY1157\njV//+tc88MAD/OEPf+DLX/5y0jjSTZs2ndX5jwq/NyWgDdC6plMuqrnzjXCWPw/mXJYc1nIrjK6P\nZ9utPdYF/GzG+1os4Mk3bkVD//9D0Ae9bQmhr7X/1m3et+42tutI+vGOrISunYXmGL/ClCBYbPzs\nxrDLvzg3SaATQkwa91y5MGkMHYDLbuWeKxee9XN7PP1XT+12e7zEt8ViiXcDtFgshMNG95ybb76Z\ntWvXsmXLFt75znfyX//1X8yZMyetNHji48TXGMpQzzHmhrrqfRZeeukl/vCHP/DKK6/gdrtZt24d\nixYtYu/evQPu/8lPfpJPfepTXHvttbz00ktJX2wTfx+pv6vY72c83bnqzqQxdABOq5M7V915xs+5\nYMECtm/fzq9//Wvuu+8+rrjiitE41TFVePddSWPoAJTTSeHdd53xc45lsJ09ezbV1dUALF26lCuu\nuAKlVNKFgZdffplnn30WgMsvv5z29na6uoxu4cNdWDhw4AC7d+/m7W9/O2CMnS0pKYm//rvf/W4A\nVq9efUYXIkZy/sOKhI1AFg9ox5LDWmcD9KV0g7fYjRa0nAqYfWl/WMutMNZll4HDfdrv54zUvHf8\nxvY63OCogryqofeLRsF/avDQ19NqdHvv+WP6zxZAWc2glxr6UoJgVvHwrZhCmCTQCSEmjVjhk8lQ\n5bK+vp45c+Zwxx13cOzYMerq6pgzZw7Hjh3jlVde4cILL+TJJ5/k4osvTjs2JyeHvLw8/vrXv3LJ\nJZfw+OOPx1vrAJ566ikuu+wyXn75ZXJycsjJyRm/NzYaV70H0NnZSV5eHm63m/379/Pqq6/i9/v5\n85//THt7O9nZ2fz85z9n+fLl8f3Lyozf62OPDTJ2b5KIFT4ZzSqXTU1NzJgxg1tuuYXc3Fy+853v\nUF9fz9GjR6mqquKpp56K7/vWt76VJ598kvvuu48XXniBjo6Os35PZyJW+GQ0q1yOZbCNXRiAwS/c\njOT4wS4saK1ZunQpr7zyypDHW63WQV/PZrMRjfa3fiVODD6i849GIBLsvwW88OyH+8eydTelt665\nZhh/93mzoeqS5LCWUw6eQmlBGorFYoyt88wElg29b7A3YZxfYuiLjflrhuZao2Uw9fcEkJE9QGGX\nlNCXWWT8TuV3dk6TQCeEmFSuX1k2KaYpePrpp3n88cex2+0UFxfzhS98ga6uLhYuXMj3vvc9br/9\ndpYsWcLHPvaxAY9/7LHH4kVR5syZw6OP9nfNczqdrFy5klAoFO+eNa7G4Kr3+vXr+f73v8/ixYtZ\nuHAhF1xwASUlJWzatIkLL7yQ3NxcVqxYEd9/06ZN3HDDDeTl5XH55ZfzxhtvjOr5jLYNczaMakXL\nXbt2cc8998TDwn/+53/S3NzM+vXr8Xg8nHfeefF977//fm666SaWLl3KW97yFiorK0ftPE5XzjXX\njGpFy4kOtpdccglPPPEEX/rSl3jppZeYOXMm2dnZIzp24cKFnDhxIn6BJxQKcfDgQZYuHbxrX1ZW\nFt3d3fHHVVVV8TFz27dvT/87CJtBLdxndI30HoeON43HzXXpXf8C3XD8NSOcVV1sBrWE7pDZZVLh\ncTw5PDBjtnEbSjQCvvZBQp9531wLh36XPl4RwGIzgnhWQvhLDX2xm905Nu9VTCgJdEKIaa+qqiqp\n8EhPT/9/iKljWGLb7r33Xu69996kbV1dXdhsNn7605+mvUZqF6gVK1bw6quvDng+t9xyS7xAynSR\nkZHBCy+8kLZ+3bp13HbbbWnrr7vuOq677rq09YP9PgbaNpVdeeWVXHnllUnrenp62L9/P1prPv7x\nj7NmzRrAKLbzu9/9biJOc8xNdLDdtGkTt99+OzU1Nbjd7tNqLXY4HDzzzDPccccddHZ2Eg6Hueuu\nu4YMdJdddhnf+MY3WLFiBZ//3Of4u2vfyU9+/AhLFy9i7ZoVLJg7G07VG10lwwFo22McGOw1inv4\nO4xtSoE7zyg+Er/ZwXsY7qo72x+LGG+WhG6YxdVD79vXk1Dhc4Dqnl2N0LQDek8M3OrnzBm8sEv8\ncTG48ox/Z2JKUGNV/vhsrFmzRm/dunWiT0MIMQr27dvH4sWLJ/o0RsXRo0e5+uqrz6oq5bp16/jX\nf/3X+Jd1IWIefPBBHnvsMYLBICtXruSHP/whbvc4jVWaRHp6esjMzIwH2/nz53P33XdP9GmdHq0h\nGjIqRIb7jPt410hzOa2whjJCWSycpYY1q2PYEvrT6fNWnKVI2Gz1Swx9CeP8EteFfOnHW+wJrX2D\nhL7YNltG+vFTROfmzaPajXy0KaW2aa2H/cIggU4IMabkC4YQ01fY6yXc2ooOhVB2O7aiImxnWbF1\nSgTb+Ni1AYJabDl13jVlHSCspSyfZYuIfN6KM9LXPXR1z1jrX+9J0v5dAzhzhwl95v0ka/Xr3Lx5\nwEJPJf/05UkT6iTQCSEmBfmCIcT0FPZ6CTWmFN1QFuxlpWcd6kaqvb19wEIqf/zjH8nPzz+zJ01s\nXUssOBJOeDxQ2fohw9rwrWujQT5vxZiKhI2unINV90xcF06fggirY5AxfrHHZij0FILNMWZvQ2tN\nuLmZN/7uPUQGGItrKy1l/ot/HLPXPx0jDXQyhk4IMea01uNbml8IMaZ0OEy4uSV9jI6OEm5pwZqd\njRqHqnv5+fnxeeNGLBpJD2vDtq5Z+oOZwzNwl8gJ/oybjBfoxTRjtUF2iXEbitZmq18s4KVM5t7T\nCh1H4fj/ge/kwM/hyhs69MVaAZ05w/7tRf1+Anv24K+txb+zFv/OnYRPnBh0/3Bz+ryjk50EOiHE\nmHI6nbS3t5Ofny+hTogpKhoMEvX5iPp86N5eon19g+6rw2EC+/ZhcTqxuNwotwuLy4VyOMb+M0Br\niIYHDmvhIVrXLGZAs3vANUBYs0zur0taa9rb23E6pYKhmASUAme2cZs5f+h9IyGj1W+g0Bdbd+wV\no9UvMsDnjjUjKeRpTyEhnxt/cx/+N734DzcSqD8GEePik72yEvcFF+BasZyT33mIiDe9aqgtf2SV\nbieTyf0JJYSY8srLy2loaODEEFfDhBCTiNbocBgdDBINBtHBIETMEKSUEcwcDqK9vcYky6ksFiwu\nF/rUKXQoZISsxGPt9vhznHYrno4aLWzRCOiwuRzuXxcNM2DrmsUKymbcW1LuldW8wh8B/OZt6nE6\nnZSXl0/0aQhxeqx2yC41bkPRGgKdaaEv0nacwIF6/K814TtWR6A5RKTPnDPSFsWZHyJ/YRBXfhBX\neSa2gi7IPAiuTqzVJ2j+WwY60v85pKxRCmsGmBB+kpNAJ4QYU3a7ndmzh5mDRwgxYaLBIIHdu/Ft\n24Z/6zZ8O3YQ7epCAfaCmbhXr8G9ejXu1avIWLgQZTXGgg1ZUOCyywCjta7vyBH8tbUEdu3CX1tH\n3+HD8SBor6jAVVODq6YaZ3UNzlkzsQTa+ifG7myAzth9wwDdsxRklSRMjl3eP0F2bHkEXbKEEJOc\nUmhHFn0dbfhrW43PlNpa+g4fiV80csybS+Y1K3AtW4ZrQRkZM50o3wkGnOah/Qg5ZR1wnou2uizC\nPis2d4TCmm5yCgPDnMzkI0VRhBBCiHNIpKsL/86d+LZuw7d9G4G6XUYrHOCYPRv3mtW4Vq3GvWY1\n9vLyIbtJdn7vi7Q9+gvCPRpbpqLwtneT8/GvDrxzKABdjUSbD+Kv3UZg9z78B4/jP9ZBuDts7KM0\nztwQrvwQzvwgriIbjspSVOoE2bHQll1qXOEXQkw74Y4O/Dt3GmPfamsJ1O0yegYA1pwcnCuW41pu\n3qqrsWafZlfJB5cZF4xS5VTA3Wc+PdFokqIoQgghhCDU2opv61b827bj27aNvoMHjSvaVivOpUvJ\nu/lmM8StwjZjxsifuO5pcrwPk3N1QhfFjh/CC2HInWW2qh3rb13rNbpdWwAP4MlQ8JZiuKqckCrE\nfyqDQEsE/7EOOg830HHYuEpuyQJXdS7Omtm4apbjKq/BdqYVLIUQk5IOhQgcOIi/tj/Ahd48Zmy0\nWnEuXEjOddfGA5x91qyzH5N7xUbCv/wktkh/i1zY6sR2xcaze94JMKJAp5RaD3wLsAI/0lp/I2X7\nR4GPY3RA7wE+orXeq5SqAvYBB8xdX9Vaf3R0Tl0IIYQQibTWBI8cwbdtG77XX8e/fTuhJqNim3I5\ncS+ZT9Yt1+FePAvXvFIs1qgx8XW4AQ4ehnDAuIX85nrzPuRP2BYw1rfuMcesJQj3wf9931i2u/tb\n1YprUrpClkN2Wbw0ud28xa6v60iEYH09/ro6/LV1+Hftov2HP4qP5bOXluJcXmMEvJpqnEuWYHG5\nxv4HLIQYFaHWVqPiZKz1bfdutFlsyVowE/eKFeTdcAOu5ctxLls2Jn/fz0Uu4uXQh7mLn1Gq2mnS\n+TwUfR8XRy7i+lF/tbE1bJdLpZQVOAi8HWgAXgdu0lrvTdgnW2vdZS5fC/yj1nq9Geie11ovO52T\nki6XQgghpo1oJDkghQL94SgxIA0WnOLr04/XAR+BJh++Bj++pjD+Fh0vCGDNiOAuCOIuCOIqCOLM\nDaFGWoPEYge7C2wZYHOB3dm/bMswth363SAHK/hs/ahPIhz1+Qjs3Yu/bpcR9OpqCZthFauVjIUL\ncFXXGGPyltfgmDNnXKZOEEIMLdrXR2DP3nh48+/cSbilBQBlt+NcutRoeTO7UNpKSk679U1rjS8Y\nwesP0dEbxOsL4fUH6fCF8PYGjfU+Y32HL0inL8TR9l6iA8SgslwX/3vv5aPx1s/aaHa5PB84rLWu\nN5/4Z8B1QDzQxcKcycOA08gLIYQQE0jr0wpLI2qlGnB9ynNFQ2d+zsqSEKicRKIZ+E/Y8LWCvymM\nv6kPHTb+y7Xnu8isycc9rxD3glLspQUouwucnMKVAAAgAElEQVRsTjOcOftvdmdyOEta7xzZJNiD\njj8pB/dpdN0cIYvbjXvNGtxr+r/bhE+cwG8WWwnsqqNryxa8Tz1l7O/x4Kyu7i+6UlODvbBw1M9L\nCNFPa02ooSG59W3/fggZn4P2sjLcq1YZ4W3FCjIWLcLiSJ5EPBiO4vX3GeGr1whlnWY4i4WxDp+5\n3tcf1IKRAarumjwOK7luB7luO3luB2W5LupP9g64b5N36lW6HUmgKwMSP7EbgLWpOymlPg58CnAA\nibF2tlJqB9AF3Ke1/utAL6KU+gjwEYDKysoRnbwQQohh1D0Nf/yyMYYppxyu2Ag1753Yc9LamHto\nlFqpRtz6FT7LymVDBSKHB9wz+wNRPERlDBGcEtdnJAW32PGhkx34d2zHt207vle30rf/AEQDYLHg\nXLyY3JtXGVUoV63EVlAwOr+fEXp97idZtu0+XCoYX+fXDnbP/STnjdM52AoKyLr8crIuN7526GiU\n4NGjRjfNOqOIQvsjj0DY6BpqKy6Ot+A5q6txLV2KxeMZp7MVYvqJ9PQS2L27v/WttpZIezsAyuXC\nvmQp1hvfj2/eIryz5nMqI9toNfMF6agP4t29O95q5jXX9wYHmCvS5LBayHXbzZuDqpluVrhyyfUY\nQS3XZazPc9vJ8xiPc9x2MmzpF6l2HHuRxgHCW2nu1Ou+PZIul+8B1mutP2w+/gCwVmv9iUH2vxm4\nUmt9q1IqA8jUWrcrpVYDzwFLU1r00kiXSyGEGAV1T8PmO4zAE2N3wTXf7g910UhC8BlJQEoNW2cY\nvPTgV1KHFe8OmBiOBg5EZxuoko4f49L3WmuCbxzFv32bWYFyO6FjRlEA5XTiWr4c9+rVuFavwrV8\nBdbM8QkioUgUb+IVcrP70lee38u64Et81vZ0fPzJN8Pv5SXHOj719gV4MmxkZthwZ9jIzLDiybDh\ncRjrPBk2HLbx6Q4ZDQQI7N1HYJc5Hq+ujlBDg7HRYiFj/vx4C56rZjkZ8+bGp2YQQhifTb3BCKe6\nA3QdPIyvtpbo7l3YD+7F1fAmyvw8b59RwtHC2ezPn0VddgV7HPlE1MB/SxYFOWb4irWa5brt5LqM\nMJZrhrH4enMft8N69sVQTM/taOTzv9iFP9QfIF12K19/dzXXrywbldc4WyPtcjmSQHchsElrfaX5\n+PMAWuuvD7K/BejQWucMsO0l4DNa6yHTmgQ6IYQ4DaEA9LZBzwnzvs24f/khCPak768s4Mg0AtYo\ndgccWYgaaHmgfc6yO+AUoEMhAvv349u6zQhx27YTOXUKAGteHq7Vq3CvMuZ/cy5ZgrKfXXn+aFTT\nHQjHx5UYV8RjY0pCCcvJ40y6+8LDP/kZcFgteMygFwt5nlj4c9iS1mdmWHEnrbOmHGPDahn5l7zw\nqVP46+oI1NUZY/J27SLa2QmAcrtxLV1qtuIZrXn24uIx+RkIMd76whGzJaz/M6DDfGwsB+Pb+051\nkN9wmPLmwyxof5OFp46RafZ06La7OJBXyf68St4smkNb+VwceblmAIuFMbO1zGMEtcTglu20YzmN\nv9mx8tyORv7ltwdo8vopzXVxz5ULJ02Yg9EdQ/c6MF8pNRtoBN4H3JzyYvO11ofMhxuAQ+b6AuCU\n1jqilJoDzAfqR/42hBDiHBXsNYPZif6AlhTYTvTf9w3Z6SGdjsLKW86+VctikwmbT0O0txd/ba3R\nfXLbNvy1tWi/0Xpqr6gg85JLjBC3Zg2O2bMHvQqttcYfigwYwmJfzhLHmRjFAYxtAxUAAOPXmO00\nvoDluB3kZzqYV5hJjnmFPM9j7182v5C9979eobkzvRtraY6T5++4hN6+MD194YT7SHzZFwzTYz6O\nbw+G6fSHaPL6k44d7JxTOe2W/pDn6A9+SYHRYe0PgTnz8bxtMZ4NN5PpsOJua8JxeD9q3x7Ce3bT\n/thP4uN+bIWFOGuqzaqaNTiXLRu3FlIhBhKJajr96X/7aZ8J/iAdveZ6fwjfIN0ZLdEI831trOxu\n4KKOY8w58QYzO4zCJVpZ6C2bRd9br6Bv8TKcy5eTO38O6zOd3Oiyj1tr+1iw5+zEM+9bZPW24PEU\nY8+5E2O02dQyoonFlVLvBB7CmLbgEa31V5VSXwa2aq1/pZT6FvA2IAR0AJ/QWu9RSv0d8GVzfRS4\nX2u9ebjXkxY6IcS0ozX0dacEtJRglhjcQgMP1saVB55CyCwET0HKfSFkFhj3ngL47ppJP2nqdBY+\neRLf9u34txmtb4F9+4yy+0qRsWgR7lWrcKxcRd/iZXRn5iVdMU9sNUu8Yt5hfikLhgfvsup2WMlz\nO4wA5ukfT5J6hTy+3tz3dFq4YHy6K2mtCYSiKcHQCH89fRF8iWExmLB9gBBpHDf42JxUOVbN4t4W\nlniPMffUMWa1vUG+t804L6XoKiqne/ZCAnMXEVm4BMvceWS6HQO0Ltpw2i2j1k1MTC9aa3r6wmmt\n46mtaEaVRvMzoTdIV2DwlnOLIt6Vsb/bYuzv3fibnxnsYcabB8g8sh/bgb1E9+1FB4wLTNb8/P4J\nu81pA6bjBYzNhzfzwKsP0Bfpi69zWp1sessmNszZMIFn1m/UulxOBAl0QogpQWsIeAdpOUtsUTPv\nByzKoYyKgLGQlhrMEgObe2Z83q4RGckYOjEqIpEoHYfq6XhtK4Ft22BXLbYmI0xHbHZOVszjeNkC\nDhfPZU9uJc1h+7CD/+1WNaIwFltvtK4NPPh/rEz27kqpolGNL5QS8mIthcHEFsWU1kNzv2inl4Km\nI5Q31zOr7Q3mnTpGTtAHQMBq53Buebwb2sG8CtrMqRssisFbChNbElPCYGLX0sR14/k7FiMXCEUG\nbzH3p1dsjF28CQ/RDJ2VYYsX/OhvJTda0vMG+UzIyrAldWeMBoP07dtnThlgTtrd2GhstNtxLl7c\nH+BWLMdeVjYtLkD4Qj6ae5tp7m2mqaeJlt6W+OOW3hYaexoHPK7EU8Lv3jPYtCzjSwKdEEKciWgU\n/B3DBDRzfe8JiATTn0NZjPA1XCtaLKRZR9L7/QxNxiqXk1hsLqOBrpAnjjPx9vhxHX+DoqP7qWg8\nxIK2I8zo6waMsSV78mcbtxmzOZxXjifTlTDov/+KuRHGYl/Gxm7wvxgboXCErvo36d5ZS19dHeE9\nu1GHD6JCxudCKDuPzqoFtFfMo7l0LscLq/BaHIMGxqHKrieyW1W8a2lSt1JHwjjEjPRxiGnrHDbc\nGVbs1qnbZW4shCNRszvjIF0Y4y1l/d2ZO3xBAqHBf38ZNkva33hqq1leymdCjst+2r8brTXhpqaE\nOd9qCezdi451Hy4pSW59W7IYi9N5Vj+viRDVUU76T6YFtebeZpp7jPuuYPJwBKuyUuQuoiSzhBJP\nCc/XPz/gcysUdbfWjcfbGJYEOiGEiIlGwHdq5CEtOkBXFovNCGWDBbPE9e4Z06Zwx1TWF46Y48gG\nGGfiD+LtTf+S1jnIXEYZ4SALO46xsvNNqk+9wdwTb+AMGd10enIL6Jy3hMCiZVCzAs+8eeRkZsSv\npE+Wwf9ifOhgkMCBg/FpE/x1dQTfeCO+3TFnTsLUCTU4Fy6IF7wJhqPm+EIj4KV3ITW6jaZ1QU3Z\ntzdoBMahWn4SOWyW/pZChy2lVXDwwjWJrYaxbR7H6RWoGcrZtgBrrenuC/f/rfv7uywaLWWJ85n1\nt6Z1D9Gd0WpRZmn8oVvM4xUbzdY1p31s/k+I+nwE9uxJCnDhEycAozquc9nSpABnLyoak/MYbb6Q\nLy2otfS20NTTRHNvM62+VsIp/1dn2bPiYa3YU0yJp4TSzNL44wJXAdaE/5vf8cw7aO5tTnttaaEb\nJRLohBDDioTBd3L4giE9bcZ+A5XJtzpSgtkgAS2zEJy5YJGr2BMhEtV0JXzxGuqKeWKr2mCD/6F/\nLqO81C9g5hez/IiPwjcPkHNoL479u+DAfuPfnFJGmfvV5vxvq1dhLykZx5+GmIoinZ34d+82qmqa\nUyfEKpqqjAycS5b0T52wfHS6vGmt6QtHkwNf2jjD9JbC2LrEYNnbF6YnGGakXxldduvQLYWDdC11\nJxSzefnQCb72wv6kVi+nzcInr5jPyopcc0xZekGQpM8Cf4jIUN0Znbb4hZf0MGbOY+ZOKJ/vsZOV\nYZuwlnOttTnPYv+cb30HDhpjcwH7rMqErpMrcC5YcNbVccdCVEdp97cPGNRiIc7b5006xqqsFLoL\n08JabLnYU0yWI+u0zmNL/RY2/W0TgUj/cAgZQzeKJNAJcY4KB81WssECWsJ63ylggM8vm2uIYJay\n3pkjVRrHUeLg/8QCH7GuS/FxJilXzLsCoUG/SMYH/7uSuzEldlsaKLC57P3dGbXWhBobjeIl5vxv\nwSNHAFB2O87q6vj8b+6VK7HmpM3KI8RpMf7NNRGoqzWmTairI7BnD7rPaPW1zpiBq7o6obJm9YT/\nu4tVVx2o2MzABWhS1gWTWxKHuuByulx2a0pLWcLk0oljzzx2csx5znJcdmyTvKtppLsbf12d2fK2\nk0BtHRFzeg2Lx2O08ia0vtny8ib4jA3+sL+/da0nuYUtdh9KmTLHY/dQ4ilJC2qxW4G7AJtl9Icn\nbKnfwre2f4uW3haKPcXcuerOSRPmQAKdEGKyGGyOtNSCIT1tRoGRgTgyhx+LFlvvyJSQlmCsilbE\nBv+nlsSOV2nrTQhrZoDr9AcJRYYf/D9YAZA8t4OchAIguS4HWU7baXdn1JEIfYcO9c//tnUb4Taj\neqElKwvXqpX9879VV2PJyDirn5UQI6FDIfoOHTK+wNftwl9XS/BIPbGrGY5Zs3Aur4kHvIxFi7A4\nTqNI0iQTiWp8wUG6lgbD3P1U7aDH/vc/XJD0GTFW3RnHk45E6Dt8BH/tznjrW/z3rxQZ8+biWrEi\nHt4cc+agrOP/vqM6yqnAqQGDWmw8W0dfR9IxFmWhwFUwYFCLdZE83da1c4UEOiHE2BmNOdIyckbQ\n1dFc73CP7/ubJkZSVj42+H/olrL07o6Jz5lqsMH/Sa1mZnemsxn8P1LRvj4CdXX987/t2EG0x5hw\n3VZU1N/6tmYNGfPmTciXJCEGEunuNsdH1ZlBr5bIiZOA0XqcsWQxLnPyc1dNDfbKymlTSOeib7xI\no9eftr4s18X/3nv5BJzR6AqfOhWvOOmvrSVQV0fUZ1RMtebmxitOupYvx1ldjTVrfAJPIBxIaklL\nrRDZ0ttCMJpcDMxtc6eFtWJPcXz8WoG7ALtl8nX9nAok0AkhRm605khz5g7QepYQ1mLbPAXGJNVi\nTESjmrbuPq7+zl852ZNehdNuVZTkuPD6hp7LaMSD/13m+jEe/D9SEa8X344d/fO/7d4dr/DmmDfX\naH1bsxrXqtXYy0qnzRdgMf1prQm3tBgBb1cdgdo6/Hv2xCeot+bkGOPwamriY/ImSze80zUe8xyO\nl3ihnISxb6Fjx4yNVivORYuSAtxYBXOtNacCp5KCWmJwa+5t5lTgVNIxCkWBu2DAVrVYcMt2ZMvn\n6BiRQCfEuW7U50gbphXNU3B6c6SJMxaJalq6AjR2+Gno8NHQ4TeWvcZyszcwbPnz61aUTurB/6cj\n1NSEb9s2o/Vt23b6Dh0yNtjtuJYswbVmtdEKt3LllP1yK8RgdDhM35EjRlCoqyNQt4u+w4eNKVgA\ne2UlrurqeCtexuLFU6Yb8VSb5zAm1NKS3PqWMD7SVlBgdJ2Mtb4tXYrF5RqV1+2L9NHa20pTbxPN\nPc0DVolMnEQbwGVzDRrUSjwlFLmLsFuldW2iSKATYqo4nXnCptscaWJAoUiUls4ADYmBzWssN3qN\nwJZajrwgK4PyPBdluS7K89yU57l48PcHae9N/zcwlbss6WiUvkOHjbFvZhfKcLNRdtri8eBauRL3\n6lW4Vq/GVV09al+UhJhKor29+Pfs6a+quWsX4ZYWY6PdjnPhQlw1NfGiK46qWSip4ntGooEAgb17\njQC30xj/Fm5tBUA5HDiXLk1qfbMVF5/RhTKtNR19HUYw6xl43rX2QHvacQWuggGDWqw7pLSuTW4S\n6ISYCuqehs13QChhnIDVAYuvhezS9MDmOylzpE0DwXCU5k5/PLAZLW3+eHBr7vSTmNeUgqIspxHY\n8lyU5xmhzQhvLkpzXQN2c5wOXZaiwSCB3buN1ret2/Dt2EG0yxiXaS2YaU4dYBQwyViwAGWTCxBC\nDCTU2pY0N15g1674mC1LdjauZcvMoivGzZafP8FnPPlorQkdPx6f781fW0tg/34IG/8v28vL+6cN\nWLkC58KFqBEWrglGgrT2tsZDWlOvOW6tp791LbG8Phgl9lPDWuK8a0XuIhxW6TkzlUmgE2IqeHAZ\ndB4feJvMkTZlBUIRmryJIS2hW2SHn9buQFIZfouCkhwzrOUmBDYzvJXkuHDYzuz3O9W6LEW6uvDv\n3GlOH7CNQN0udNBoZXTMnh0f++ZevQp7RYVcWRbiDOlIhGB9vVkW32jF6zuYMKdZWVl88nPX8hqc\nS5ZgcZ5bY58jPb0Edu9Kan2LdBgVHJXbbXZlNVvfamqwzZw54PNorens6xw0qDX1NnHSfzLtuJmu\nmYPOu1biKSE3I1c+A6c5CXRCTAWbchlwLjUU3N8h5fcnKX8wQqPXx/GEkBbrDtnQ4edEd/IYBZtF\nUZLrTOoOmbhcnOMcswqPk12otRXf1q34ze6TfQcPGuM/rVacS5fiXrXKnMR7NbYZMyb6dIWY1qI+\nn9F90GzF89fVEm4yujRjtZKxcIHRgmeGPMecOdOmq6aORo2Am9D61nfoUP+0EXPmxCfsdq1YTsbc\nufEeAaFIiBZfy4Bzr8VCmz+cXLEzw5qR1KqWWs6/2FMsrWtCAp0Qk17QB/88a+BxbjkVcPfu8T8n\nAUBPXzhecKTRm941MnVcmt2qKMuNtbC5E7pGGstF2U6spzlX2nSktSZ45Ig59s0IcaHGRsC42u1e\nsdxofVuzGldNDRa3TFchxEQLnziBf9cu/LV1BHYZc+TFpv2weDw4q6uNkLe8Bmd1NfbCwgk+45EJ\nd3SYYwzNAFdX1/++srPjXSedy2sIL6qi1dqbFtRi49lO+E+gUy7OznDOoNRTSklmSXJYM7tI5mXk\nSeuaGJYEOiEms2gEnv4g7H/e6FqZGOrsLrjm24MXRhFnrSsQouFUf6GR1CqRXl8oaX+HzZLWqha7\nleW6KczKOO3Jrc8FOhgksHevWYFyO/7t24l4jcnjrfn5uFetinehdC5eJOPfhJgCdDRK8I034pOf\nB2rrCBw8GB9HZispiU+b4KqpMao4TvDFGR0O03fwYFLrW/DoUWOjxYJl3mz6Fs3i1NyZHJ/loj4r\nQLO/JV58xBf2JT2fw+JID2oJYa3IXYTTdm51TxVjQwKdEJPZC/fC//0nrP9no0jJSKtcimFpren0\nh+Lj15KrRBqPu1PmXnPZrQnFRoyQFl/OczHTI4FtJCI9Pfh37MS33Zg+wF9Xhw4Yg/jtsyrNAiar\ncK1ahaOqSq5OCzFNGJUe9xkteOYk6KGGBmOjxULG/PkJrXg1ZMybi7IOX5xrS/0WvrX9W7T0tlDs\nKebOVXeyYc6GYY8LtbXFw1vPzu0E9+yFgNEVvi/bRcvsbOrLbdQVBdiW10kgpWfjDOeMQcNasaeY\nGc4ZWNT06GoqJjcJdEJMVq/8B/z283DBP8L6r0/02Uw5WmtO9QaTSvknFhxp9Prp6UsObB6HNall\nLbE7ZFmuixkeh4SLMxBqa8O/fXu8C2Xf/gPG1BoWizFR7prVxiTeq1dhKyiY6NMVQoyj8KlT5rx4\nRjdN/65dRDs7AbC43TiXLYtPfu6qqcFeXJx0/Jb6Lfz2B1/kPS/2kd8F7dnwzOUZXPmRr8ZDXTga\nptXbQNvO/6Nn53b07gO4DxzH0260qIUtUF8Mh0oVh8oUh0oV3jw7xZkDB7XYvcsm052IyUECnRCT\n0d5fGV0tF18NNzwm0wcMQGvNiZ6+lFL+vv7lDn9SGX6AbKeNsqRiI8ldI3NcdglsZ0lrTfCNo8b8\nb1u34du+ndCxYwAopxPX8uX9878tX4E10zPBZyyEmEy01gSPHiVgjsfz19UZJf9DRhd3W2Fhf1XN\nmhq+8dxdvPt5L86E63MBGzxzmZ3M4gpyD7dR9mYvVa0au/lfwolsOFrhoH1OPv6FFdgWLaAor9wo\nPGKOZ5PWNTGVSKATYrI5/jo8djUUV8Otm42xcuegaFTT1t2XFNISi440ev30haNJx+S67UY4y3Wn\nzcNWZgY2Mbp0KERg/358W7fFJ/GOnDoFgDU3F9fq1fH535yLF494riUhhIiJBoN07d5Jy+t/pbe2\nFrX3EK4WY5ytBoa6DBd2WOmeU0RkyVycy2uYsXotJbOW4rZLMSUxfYw00MkIdCHGw6l6+O8bIasE\nbvrZtA5zkaimtSuQPmm211hu8gYIRpIDW77HQXmei8Ul2bxtSVFSAZKyPBeZGfJRNdaivb34a2vN\n7pPb8NfWov1GmW17eTmZl1wSnz7AMWeOtHgKIUasJ9jD8e7jHOs+Ztx3HTOWu47T5m8DJ7DWuJVH\n8lnTkctNPzwy4HNpYPazz+BcsABll4t5QoAEOiHGXm87/PQ9xlw2738GPANPPDpVhCNRmjsDaXOv\nxZabvQHC0eSW/4KsDMrzXCwry2H9spL4OLaKPBeluS7cDvkoGiudmzfT9uBDhJubsZWUUHj3XeRc\ncw3hkyfxbd+O36xAGdi3z5hQWCkyFi0i993vjnehtBcVTfTbEEJMcp19nfGgdqz7GA3dDfHHpwKn\nkvad6ZpJZVYlF5ZeSGV2JZVZlVRkV1CRVUG2IxuAul9eiL3Nm/Y64cJcXEuXjst7EmKqkC6XQoyl\nUAB+ch007TC6WVaunegzGlYwHKW5059QaMTsGuk1Hjd3+knMa0pBUZYzodhIf3fIcjOwOe0yVnAi\ndG7eTPOXNsYrTQJgtWLNyyNy8iQAyuEwqs+tNud/W7ECa1bWBJ2xEGKy0lpzKnAq3tKW2Mp2rPsY\nXcGupP2L3EX9YS2rIml5JN0iOzdvpuG+L2Lp659GJpphp/wrXyXnmmtG/f0JMRlJl0shJlo0Cv/z\n/+D4q3DDjydNmAuEIjR5/YNWiWztDpB4nceioCTH6AK5dvaMtCqRJTkuHDYZYD6RtNZEvF5CDY2E\nmpoINTYSamzE+8wz6L6+5J0jEaLd3RR+5tPG/G/LlmKR8W9CCMyiVP4THOs6lhTcYsu9od74vhZl\nocRTQmVWJVfNvsoIbVmVVGZXUpZZdtbzsMVC20A9DIQQyaSFToix8rv74G/fgXd8Bd7yyXF7WX8w\nQqPXx/GUUv6x4HaiO/kLvs2iKMl1Jk2anbhcnOPEbpXANpG01kTa242gZga2YMJyqKkZ7Uue+NaS\nmUm0p2fgJ1SKxfv2jsOZCyEmm0g0QquvNT6eLdbCFmttC0T6W/RtykZZVllSWIstl2WWYbfKGDYh\nxpK00AkxkV77oRHmzvsHuPATQ+763I5G/uW3B2jy+inNdXHPlQu5fmXZoPv39IXNapDJpfxjga29\nN5i0v92q4tUgL19YmNbCVpTtxCqTZk8oHY0SPnEyKbAltrSFmprSWtosOTnYy0pxVFWRedFF2MvK\nsJeWGvdlZVizszl0+RWEm5rSXs9WUjJeb00IMQHC0TDNPc3xoJbYytbQ3UAo2t+N0WFxUJ5VTmVW\nJReUXGAEN3NMW4mnBJtFvioKMdnJX6kQo+3AC/DCZ2HBVXDVPxuDzAbx3I5GPv+LXfF51Rq9fu79\nRR2NXj8LirKSu0OaAc7rCyU9h8NmibeqvaM0Jz73mrHOTWFWBhYJbBNKRyKE29rSQlqspS3c1IwO\nJf9erXl52MvKyJg/n8x161ICWynWzMxhX7fw7rvSxtApp5PCu+8a9fcohBhfwUiQxp7GpKqRsVa2\npp4mwrp/AjeXzUVFVgVzc+ayrmJdPLRVZldS6C6UedmEmOKky6UQo6lxO/x4AxQshL/fAo6hJ1e+\n6Bsv0uj1D7mPy25NKDZihLT4cp6LmR4JbBNNh8OEWloJNTUSamxKb2lrboZwOOkY68yZZkArxVFW\nhq3UuLeXlWEvKcHiGZ2JuQercimEmPwC4YBRLTK13H/3cZp7m4nq/ilgMu2ZacVHYsszXTNlqhEh\npiCZWFyI8dZxFH70NmOOuQ//ETILhz1k9r1bGOwv8Jcfv4jyPBczPA75j3iC6WCQUGtrf+taY0pL\nW2urUfI/ga2wMKkLZPJyCRbn2RUMEEJMD72h3rSwFltu87Ul7ZuTkTNg1cjK7EryMvLk/wohphkZ\nQyfEePJ3wBM3QCRktMyNIMxprXE7rPQGI2nbynJdLK/IHYszFQOIBoOEm5qMQiPx1rWmeCtbuLWV\npNKfSmErKsJeVoZrzWqyzbDmMIObraQES0bGxL0hIcSk0hXs6i8+khLc2gPtSfvmO/OpzE4Yz2YG\nt/KscnIyciboHQghJjMJdEKcrXAf/OwWo4XuA88Z3S1H4MHfH6Q3GMFmUUkTcbvsVu65cmTPIUYm\nGggkjF9LH8cWPnEi+QCrFbsZ2DwXXJDculZWir2oCCWl/oUQJq013j5vWgGSWIjz9iVPkF3oLqQy\nq5JLKy5NqyDpsY9Od2shxLlDAp0QZyMahef+Ed58Gf7uYai6aESHPfzyG3z7xcPcuKaCC+bM4F9/\nd3DEVS5FumhvL6FYC1ticDOXI+3JV8Cx27EXFxuB7ZJLjJBmtq45ysqwFRWhbPLxKITop7XmpP9k\nWmiLLfeE+qcJUShKPCVUZFfw9llvj1eNjLW0uWyuCXwnQojpRr6xCHE2Xvwn2P0MXHE/VL9nRIc8\nu62Bf3p+L1ctK+Zr767GalG8a1X5GJ/o1Bbp6Ukfv5ZQMTLiTb76rez2eKua8/LL0sax2QoKUFbr\nBL0bIcRkFdVR2nxtaVUjY10k/eH+IpwJIsMAACAASURBVFZWZaUss4yK7ApWFK5IamUryyzDYZVW\nfCHE+JBAJ8SZ2voovPzvsPrv4eK7R3TI7/a08Nln67h43kweet8Kmf8N46p3tKtrkEmzjcfRrq6k\nY1RGRjykOZctSwhrZmCbORNlkTLcQoh04WiYlt6WpLAWWz7efZxgtH8uT7vFHp+j7fzi8+Pj2Sqz\nKinOLMZukYm1hRATTwKdEGfi0O9hy6dh3tvhnf825FxzMa8caecT/72D6rIc/usDq8mwnRstRFpr\nIl4voYb0ybJj99GenqRjlNuNo6wUW2kp7pUr0ifNnjFDqrkJIQYVioRo7GlMqxrZ0N1AQ08D4Wj/\nNCJOq5OK7Aqqcqq4pPySpAqSRe4irJZz47NaCDF1SaAT4nQ118LTt0LRUrjhUbAO/2e0q6GTf/jJ\nVmbNcPPo35+HJ2P6/OlprYm0t6eFtP6Kkc1ony/pGIvHEw9n7vPPTys6Ys3NlcAmhBhSX6TPmKNt\ngHL/qXO0eeweKrMqWZC3gLfNeltSuf8CV4F83gghprQRfatUSq0HvgVYgR9prb+Rsv2jwMeBCNAD\nfERrvdfc9nngQ+a2O7TWvx290xdinHmPwxPvBVce3Pw0ZGQNe8jhth5uffQ1ct12Hv/QWvI8U2tc\nhY5GCZ84OfCk2eay7utLOsaSk2MUGKmqIvOii/oDm3lvyc6WL1BCiGH5Qj6Odx9PK0ByrPsYrb2t\n6ISZPLMcWczKmkVNQQ1Xz7k6aZ62GU5p1RdCTF/DBjqllBX4HvB2oAF4XSn1q1hgMz2ptf6+uf+1\nwL8D65VSS4D3AUuBUuAPSqkFWuv0ibeEmOz8XmOuuZAPbv8tZJcMe0ij188HHv4/LErx0w+tpThn\n8k0mrSMRwm1tA3eHNCtF6lAo6Rhrbi72sjIy5s8n89JLk0v6l5ZizRo+6AohBEB3sDt5PJsZ2o53\nH+eEP3lKkRnOGVRmVXJe0XnxqpGxYiQyR5sQ4lw1kha684HDWut6AKXUz4DrgHig01onVizwQPyS\n2XXAz7TWfcAbSqnD5vO9MgrnLsT4CQfh6Q9A+2G45VkoWjLsIe09fXzg4f+jpy/MUx+5kKqZEzO3\nkA6HCbe2ml0gm9Jb2pqbIRxOOsaan28EtiWLyXzbFUmTZttLS7F4ZJ4kIQRsqd/Ct7Z/i5beFoo9\nxdy56k42zNmQtI/Wms6+zrSqkbHljr6OpP0LXYVUZFdwcdnF8aqRsZa2TEfmeL49IYSYEkYS6MqA\n4wmPG4C1qTsppT4OfApwAJcnHPtqyrEDTrCllPoI8BGAysrKEZyWEONEa9h8B7zxF7j++zDn0mEP\n6Q6EuPXR12jy+nn8Q2tZUpo9dqcXDBJqbU0v6x8LbK2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3CWF+Yfh7+ecPJBERERGpDAp0\nUn1lpeWuNZd4APp9AvXOKbY0x2F5fNk2Vv1xmMl3tqFr64BiaxM/+4yY/47AvVUrQt6ZibOfX0V0\nL1UkKTMpP6zl3yqZuJ/IxEgycjLy67xcvQjzDaN9o/b5t0g28W1CiG8IHi7FXwUWERERqUwKdFI9\nORy5z8xFb4S734XgjsWWWmsZ8/GvfPrLQZ7qdh49OxQ/1OT4hx9x8Jln8GjfjuAZM3D29q6I7qWC\nZTmyiEmKKbDI9t/h7cSBJM7GmSCfIEJ9Q+kc0Dn/ubYmfk00kERERESqBQU6qZ6+fhZ+WwE3vAjn\n31pi6ctf/c6i9ZE8eOU5DL2i+Kt4x5YsIfa58Xh16ULQm2/g5KGrMGeyvweSnDg98u/fo5OiybbZ\n+bV/DyS5IuiKApMkg72DcXXWQBIRERGpvhTopPpZPxPWvQEdh8LFD5VYOmv1Xt78fg+9O4Yw4obi\nFwI/MncehyZPxvuqqwh89f9wqlX8mnRSudKz04lIjMifHnlieEvKSsqvc3NyI8Q3hOZ1mnNt6LUF\nJkn61dJtsyIiIlIzKdBJ9bJ7JXwxElp0h64Tc4ehFGPZpigmrPyN7m0CmHBb62Jvn4ufPp3D017D\np2tXAqdMxri5VVT3UgyHdRCXEse+xH2FrrgdTDmI5Z/1Mht5NiLML4wbm95YILQFeAXg7ORchWch\nIiIiUvkU6KT6iN4MHwyCxu3gzllQwg/vX/way6jlv3BZ8/pM7Xkhzk6Fw5y1lsP/9ypHZs7E79Zb\nCHjhBYyL/pWoSMmZyfnPs514xS0iMYL0nPT8Ok8XT8L8wmjbsC23+d2WG9x8cxfb9nT1rMIzEBER\nETmz6KdXqR6O7oMlPcG7IfReCm7F/1C/9q94Hl2ylQuDa/N234uo5VI4+FlriZs4kWPzF1C7Z0/8\nx47BlLAYuZRdtiObmOSYf66ynXCbZHxafH6dk3Ei0DuQMN8wOgZ0zA9tYX5hNPBooIEkIiIiImWg\nQCdnvtSjsOgucGRDn+Xg3aDY0u1Rx7l//iaa1Pdibv8OeLoV/ohbh4PYcc9xfNky6va7j4ajRik8\nnCRrLccyjhUaRrI/cT9RSVFkO/4ZSFK7Vm3CfMO4NPDSAqEt2CcYN2fd3ioiIiJyOhTo5MyWlQ7v\n3QvHI+C+j6F+82JL/zqURP+5G6jr7cb8QR2p7Vk4LNjsbA6OHk3CxyuoN3QoDR4brjBXgoycDCIT\nIwuFtv0J+0nMTMyvc3VyJcQnhKZ+Tbk6+Op/Jkn6hlHbvXYVnoGIiIhIzaZAJ2cuhwM+fggi10KP\nORDapdjS6GOp9Jm1ARdnJxYO6kQjX/dCNTYzk5j/jiDpyy9p8Nhw6j/wQEV2X21Ya4lLjSs0QXJ/\n4n4OJB8oMJCkoUdDwvzC6BrWtUBoa+zdWANJRERERKpAmQKdMaYrMA1wBmZZayf9a//jwGAgGzgM\nDLTWRuTtywF25JVGWmtvKafepab79jn4dTlc+xy0vrPYsvjkDPrO3kBqZjZLh3YmtJ5XoRpHRgYx\nwx8j+YcfaDhqJPX696/Axs9MKVkpRYa2iMQI0rLT8us8XDwI8w3jgvoXcMs5t+TfIhnqG4qXa+E/\nWxERERGpOqUGOmOMM/AmcB0QDWw0xqyw1u46oWwrEG6tTTXGPAhMAXrm7Uuz1rYt576lpts0B9a8\nCuED4ZLhxZYlpmfRb84GDiaksWhwJ1oG+BaqcaSmEj1sGClr1+E/bix1evWqyM6rVLYjm4PJB9mX\nuK/QLZKH0w7n1xkMjb0bE+YXRnij8AKhrZFnI92GKiIiIlJNlOUKXUfgL2vtXgBjzHvArUB+oLPW\nfn9C/c9An/JsUs4yf3wFK5+A5jdAt5eKXWsuPSuHwfM28UdcEu/cF85FoXUL1eQkJxP1wAOkbdlK\nwMSJ1L79toru/qSt3LuSaVumEZsSi7+XP8PbD6d70+4lvuZY+jEiEiPYl7CvwNW2qKQoshxZ+XW+\nbr6E+YXRuXFnmvg1yb9FMtg3mFrOWjxdREREpLorS6ALBKJO+D4a6FRC/SDg8xO+dzfGbCL3dsxJ\n1tr/FfUiY8wQYAhASEhIGdqSGunAVni/P/i3yX1uzrnoj2hWjoOHF21hY8RRXuvVjitbNCxUk3P8\nOJFDhpK+axeBr7yMb7duFdz8yVu5dyXj1o7LX4PtYMpBxq0dB8B1odcRmRiZG9z+dcUtISMh/z1c\nnFwI9gkmzDeMK4KvoIlvk/zn22rXqq2rbSIiIiI1mLHWllxgTA+gq7V2cN73fYFO1tphRdT2AYYB\nV1hrM/K2BVprY4wxTYHvgGustXtKOmZ4eLjdtGnTKZ2QVGPHI2HWteDsBoO/AR//IsscDssT72/n\no60xTLitNX0uDi1Uk330KJEDB5G5Zw+B017F5+qrK7r7U3L9B9dzMOVgoe3OxhmLxWEd+dsaeDQg\n1Dc0P6z9fcWtsXdjXJw030hERESkJjHGbLbWhpdWV5afAmOA4BO+D8rb9u8DXguM5oQwB2Ctjcn7\nfa8x5gegHVBioJOzUNrx3LXmstLhvhXFhjlrLeM/3cVHW2P47w0tigxzWXGHiBw4kKyYGIKmT8f7\n0ksquvtTFpsSW+T2HJvD0AuGEuYXRhPfJoT6huLt5l3J3YmIiIjIma4sgW4j0NwY04TcINcLuOfE\nAmNMO+Btcq/kHTphex0g1VqbYYypD1xC7sAUkX9kZ8LSPnBkD/T9EBqeV2zptG//ZN7a/Qy+tAkP\nXXlOof1ZMTFEDBhITnw8wTPfxqtjx4rs/LREJEbg4uRS4Jm3vwV4BTCsXaGL4CIiIiIiBZQa6Ky1\n2caYYcCX5C5bMMdau9MYMx7YZK1dAbwEeAPv5z2v8/fyBC2Bt40xDsCJ3GfodhV5IDk7WQsrhsH+\n1XD7TGhyebGl89bs49Vv/uSui4IY3b1loWfDMiMiiBgwAEdSMiFzZuPR9swcrmqtZcWeFbyw/gWc\njTM4USDUuTu7M7x98ZM9RURERET+VqYHb6y1nwGf/WvbmBO+vraY160F2pxOg1LDff8C/LIUrn4G\nLuxZbNlHW6MZ98kurj+/ERPvaFMozGXs2UNk/wHYrCxC5s3Fo1Wriu78lCRmJjJh3QQ+3/854Y3C\nmXjZRDbHbT7pKZciIiIiIlDGQCdSIbYsgFUvQfv74LIniy379rc4nnz/F7qcU4/XerfDxdmpwP70\n3buJHDgInJwIXTCfWs2bV3Tnp2Troa2MWjWKuNQ4Hm33KANbD8TZyZnuTbsrwImIiIjIKVGgk6rx\n1zfwyXA45xroPrXYtebW7z3CQ4u20LqxLzPvC8fd1bnA/rRffiFy8P04eXoSMncOtZo0qYzuT0q2\nI5t3fnmHGb/MoLFXY+Z3m88FDS6o6rZEREREpAZQoJPKF7sDlvWDhufD3e+Cs2uRZb/GJDD43U0E\n1/Vk7oCOeNcq+HFN3bSJqKEP4FynDiHz5uEWFFgZ3Z+UA8kHGLV6FFsPbeXmpjfzdKenNa1SRERE\nRMqNAp1UroQYWHQ3uPvBvcuglk+RZXsPJ9NvzgZ8PVxZMKgjdb3cCuxPWbuWqIeH4ervT8i8ubg2\nalQZ3Z+UL/Z9wfh143HgYOJlE7mp6U1V3ZKIiIiI1DAKdFJ50hNy15rLTIaBX4Bv4yLLDiak0Xf2\nBgAWDOpIgJ9Hgf1JP/xAzKPDcQsLI2TObFzq16/w1k9GSlYKE9dP5OM9H3NBgwuYdNkkgn2CS3+h\niIiIiMhJUqCTypGTlXubZfzvcO8H0KjoKZRHUzLpM2s9iWlZLBlyMU0bFLw9MfHLr4h58kncW7Qg\n+J2ZuNSpUxndl9mv8b8yctVIopOjGXrBUIZeOBRXp6JvKRUREREROV0KdFLxrIVPHoO938Otb8E5\nVxVZlpyRTf+5G4g+lsb8gR1pHehXYH/CihUcGPUUHhdeSPDMt3H2Kfp2zargsA7m7ZzH61tep55H\nPWZfP5tw//CqbktEREREajgFOql4q16CbQvhilHQ7t4iS9KzchgyfxM7DyQys+9FdGpar8D+Y8uW\nETt2HJ6dOhH85hs4eXlVRudlEpcSx+ifRrM+dj3XhV7H2M5j8avlV/oLRUREREROkwKdVKxtS3IX\nD7/wHrhyVJEl2TkOHl2ylbV7jvBqz7Zc07LggJOj8xcQ9+KLeF1+GUGvvYaTu3tldF4m30V+x9i1\nY8nIyeC5Ls9xe7PbCy16LiIiIiJSURTopOLs/QFWDIMml8PN04pca85ay1Mf7uCrXXGMu/l8bmtX\ncOmB+JnvcHjqVHyuu5bGr7yCk5tbofeoCmnZabyy6RWW/r6UlnVbMvnyyTTxO/PWwBMRERGRmk2B\nTipG3C5Y2hfqnws9F4JL4SBmreWFlb/x/uZoHru2Of0vaVJgX/zrrxP/1nR8b7qJxpMmYlzOjI/r\n70d/Z+SqkexJ2EP/Vv15pN0juDmfGUFTRERERM4uZ8ZPyFKzJB7MXZ7A1RPufT93zbkivPXDHmb9\ntI/+XcIYfk3z/O3WWg5NeYmjc+fi1+NOAp57DuPsXFndF8tay+Ldi5m6aSq+tXx5+7q36dK4S1W3\nJSIiIiJnMQU6KV8ZSbD4bkg/DgM+A7+gIssW/hzBS1/+zu3tAhlz0/n5z51Zh4O4CRM4tngJde69\nl0ajn8Y4OVXmGRTpSNoRnl3zxwhUogAAIABJREFULKtjVnNF0BWMv2Q8dd3rVnVbIiIiInKWU6CT\n8pOTDe/3h7idcM8yCLiwyLJPth/g2Y9/5ZrzGjKlxwU4OeWFuZwcDj7zLAkffUS9wYNo8MQTZ8SA\nkTUxaxj902iSMpN4utPT9GrR64zoS0REREREgU7Kh7Xw2RPw1ze5A1CaX1tk2Q+/H+I/S7fRIawu\nb97bHlfn3KtvNiuLAyNHkvjZ59QfNoz6Dz9U5aEpMyeTaVumMX/XfJrVbsbM62dybp1zq7QnERER\nEZETKdBJ+fjp/2DzPLjsCbiof5ElmyOO8sDCzbTw92FWv3DcXXOfi3NkZhLzn8dJ/vZbGj75BPUG\nD668vouxN2EvI1eNZPfR3fRq0Ysnwp/A3eXMWS5BRERERAQU6KQ8/PI+fPsctLkLrn62yJLfDiYy\nYO5GAvw8eHdgR3zdXQFwpKUR/cijpPz0E42eeYa6fYpeeLyyWGtZ/udyJm+YjLuLO69f/TpXBl9Z\npT2JiIiIiBRHgU5Oz/418PFDEHop3PpmkWvNRRxJoe/sDXi6ubBgUEfqe9cCICc5heiHHiJ140YC\nJjxP7R49Krv7AhIyEhi3dhzfRH7DxQEX88KlL9DQs2GV9iQiIiIiUhIFOjl1h3+H93pDnSbQayG4\n1CpUEpeYTp/Z68lxOHhvSGeC6ngCkJOYSNSQoaTt2EHjKVPwu/mmyu6+gI2xGxm1ehRH04/yxEVP\ncF+r+3AyVT9dU0RERESkJAp0cmqSD8GiHuBcK3etOY86hUqOp2Zy3+wNHE3OZPH9F9OsoQ8A2ceO\nETVoMOl//kng/03F9/rrK7v7fFmOLKZvm86sHbMI8Q1h4Y0LaVWvVZX1IyIiIiJyMhTo5ORlpuSu\nNZcSD/1XQp3QQiWpmdkMmLeRffEpzBvQgQuDawOQffgwkQMHkRkZSfAbr+N9xRWV3X2+qMQoRq4e\nyY74HdzR/A5GdhiJp6tnlfUjIiIiInKyFOjk5Dhy4INBcHA79FoMge0LlWRk5zB0wWa2Rx1nep+L\n6NKsPgBZsbFE9h9AVlwcwW/PwOviiyu7+3yf7PmECT9PwNnJmZeveJkbwm6osl5ERERERE6VAp2U\nnbXw+Uj443O48WVo0a1QSY7D8vjS7az+M54pPS7ghlb+AGRGRxPZrz85CQmEzJ6FZ/vCQbAyJGUm\n8cL6F1i5dyXtG7Zn0mWTCPAOqJJeREREREROlwKdlN26N2DjO9DlUeh4f6Hd1lqe+d8OVu44yDPd\nW3J3eDAAGXv3ETlgAI70dELmzsWjTevK7hyAbYe2MWr1KGJTYnm47cPc3+Z+nJ2cq6QXEREREZHy\noEAnZbPzI/jqGWh1O1z7XJElU778nSUbonj4qnMYfFlTANJ//4PIgQMBCJ3/Lu4tWlRay3/LceQw\na8cspm+fjr+XP/O6zqNtw7aV3oeIiIiISHlToJPSRf4MHw6F4IvhthngVHic/8xVe5j+wx7u6RTC\nk9fnhra0nTuJGjgIU6sWIfPmUqtp08runIPJBxm1ehRbDm2hW5NuPHvxs/i4+VR6HyIiIiIiFUGB\nTkoW/xcs6Q1+QdB7Cbi6FypZujGSFz/bzU0XBPD8ra0xxpC6dStRQ4bi7ONDyLy5uIWEVHrrX+3/\ninHrxpHjyOHFS1/kpqY3YYpY+FxEREREpLpSoJPipcTDojvBOEGfD8CzbqGSL349yFMf7uCKcxsw\n9e62ODsZUtZvIOrBB3FpUJ/QefNwDajcoSOpWalM3jiZD//8kDb12zD5sskE+wZXag8iIiIiIpVB\ngU6KlpUGS3pBUiz0+xTqFr5d8qc/43l0yTbahdRhep/2uLk4kbx6NdHDHsE1OIiQOXNwbdiwUtve\ndWQXI1eNJCIxgvvb3M+DbR/E1cm1UnsQEREREaksCnRSmCMHlg+G6E3QcwEEdyhUsjXyGEMWbKJp\nAy/m9OuAp5sLSd98Q/R/HqdWs2aEzJmNS506ldeydTB/53ymbZ1GXfe6zL5hNh38C/ctIiIiIlKT\nKNBJYV89A7s/ha6ToOXNhXb/EZfEgHkbqe9di/kDO+Ln6UrCypUcGDES99atCJk5E2c/v0pr93Dq\nYUb/NJp1B9dxTcg1jOs8jtrutSvt+CIiIiIiVUWBTgr6eTr8/BZ0ehAufrDQ7qijqfSdvR43ZycW\nDupEQ193ji//kIPPPIPnRRcRNGMGzt5eldbuj1E/8uyaZ0nLTmNM5zH0aN5Dg09ERERE5KyhQCf/\n+O0T+OIpOO8muOGFQrsPJ2XQd/Z60rMcLBvamZB6nhxdvJi48c/jdcklBL3xOk4eHpXSanp2OlM3\nT2XJ7iW0qNOCKZdPoWntyl8WQURERESkKhVeUKwIxpiuxpjfjTF/GWNGFbH/cWPMLmPML8aYb40x\noSfs62eM+TPvV7/ybF7KUfSm3OfmAi+CO94BJ+cCuxPSsrhvzgbiEjOY078DLfx9ODJnLnHjn8f7\n6qsJmv5WpYW5P4/9Se+VvVmyewl9z+/L4u6LFeZERERE5KxU6hU6Y4wz8CZwHRANbDTGrLDW7jqh\nbCsQbq1NNcY8CEwBehpj6gJjgXDAApvzXnusvE9ETsPRvbC4J/gEwD1Lwc2zwO60zBwGv7uRvw4l\nMbtfB9qH1ObwW28R/9rr+HTrSuCUKRjXip8kaa3lvd/f4+WNL+Pt5s30a6dzaeClFX5cEREREZEz\nVVmu0HUE/rLW7rXWZgLvAbeeWGCt/d5am5r37c9AUN7XNwBfW2uP5oW4r4Gu5dO6lIvUo7CwB9gc\nuPcD8KpfYHdWjoOHFm1mU8QxXu3Zjsua1+fw1P8j/rXX8bvtNgJffrlSwtzR9KM88t0jvLj+RToG\ndGT5LcsV5kRERETkrFeWZ+gCgagTvo8GOpVQPwj4vITXBp5Mg1KBstJhSW9IiIZ+K6B+swK7HQ7L\nk+9v5/vfDzPxjjbc2LoRcS9O5NiCBdTu1RP/MWMwTmW6a/e0rD2wltE/jSYhI4FRHUdxz3n3aPCJ\niIiIiAjlPBTFGNOH3NsrrziF1w4BhgCEhISUZ1tSFIcD/vcARP0Md82DkIsL7LbWMu6TnXy87QAj\nurag10WBxI4dy/H3P6Buv340HDWywkNVVk4Wr219jXk759HUrykzrp1Bi7otKvSYIiIiIiLVSVkC\nXQwQfML3QXnbCjDGXAuMBq6w1mac8Nor//XaH4o6iLV2JjATIDw83JahLzkd34yFnR/Bdc9Dq9sL\n7f6/b/5k/roIhl7elAcuCeXAqKdI/OQT6j0wlAbDh1d4mNufsJ8Rq0bw29HfuPvcu3myw5N4uFTO\n0BURERERkeqiLIFuI9DcGNOE3IDWC7jnxAJjTDvgbaCrtfbQCbu+BF40xtTJ+/564KnT7lpOz8ZZ\nsPY16DAYujxSaPecn/bx2rd/0jM8mJHXNOXAE0+S9NVXNHjsMeo/MLRCW7PW8r+//sfEDRNxc3bj\n1ate5ZqQayr0mCIiIiIi1VWpgc5am22MGUZuOHMG5lhrdxpjxgObrLUrgJcAb+D9vCs3kdbaW6y1\nR40xz5MbCgHGW2uPVsiZSNn8/gV89l84txt0nQz/utK2fHM04z/dRddW/jx/Y3NiHh1O8o8/0uip\nUdTtV7GrTiRkJDB+3Xi+iviKjv4defHSF2nk1ahCjykiIiIiUp0Za8+8uxvDw8Ptpk2bqrqNmidm\nC8zrDg1aQP+V4OZVYPfXu+J4YOFmLm5al1l3t+LQ8EdJ/Xk9/mPHUqdXzwptbXPcZkatHkV8ajzD\n2g2jf6v+OP9rLTwRERERkbOFMWaztTa8tLpyHYoiZ7BjEblrzXnWh95LC4W5dXuO8PDiLbQO9GPG\n7ecR9+CDpG3dSsDEF6l9220V1la2I5sZ22fwzo53CPIOYsGNC2hdv3WFHU9EREREpCZRoDsbpB2D\nRXdBTgb0/xR8Ct7GuCM6gfvnbyK0ridzbj+X+AeGkP7bbwROfQXfrhW3bGB0UjSjVo9i++Ht3HrO\nrTzV6Sm8XL1Kf6GIiIiIiAAKdDVfdga81weO7YO+H+XebnmCvw4l02/uBvw8XHn39uYkPHg/mXv3\nEvTaa/hcfVWFtbVy70om/DwBgCmXT6Fbk24VdiwRERERkZpKga4msxY+fhgifoI7ZkHYpQV2xxxP\n477Z63EysOC2pqQ9fD9ZBw4QNGM63pdcUiEtJWcm8+L6F/lk7ye0bdCWSZdPItBba82LiIiIiJwK\nBbqa7LvnYcf7cM0YuOCuAruOJGfQd/Z6ktKzee/WUByPDiUnPp6Qd2bi2aFDhbSz4/AORqwawYGU\nAzx44YMMuWAILk76CIqIiIiInCr9NF1TbZ4Hq1+B9v3g0scL7EpKz6L/3I0cOJ7Gwm6BuD3xEDkp\nKYTMnYPHhReWeys5jhzm7pzLm1vfpIFnA+beMJf2jdqX+3FERERERM42CnQ10Z/fwKePQ7NrofvU\nAmvNpWflcP/8Tfx2MJE5V9XHb+QwbHY2oe/Ow71ly3JvJTYllqd/epqNsRu5IewGxnQeg6+bb7kf\nR0RERETkbKRAV9Mc/AXe7weNWsFd88D5n3/E2TkOhi3eyvp9R5neyRv/MY9hXZwJXTCfWs2alXsr\n30R8w9i1Y8lyZDG+y3hua3Yb5l8LmYuIiIiIyKlToKtJEqJh8d3gXhvuWQa1fPJ3ORyWkct38M1v\ncbzSxoUmL47AeHkROncObmFh5dpGalYqL216iQ/++IDz653P5MsmE+ZXvscQEREREREFupojPSF3\nrbnMFBj4JfgG5O+y1jJh5W8s3xLN+CZZtJ46Fue6dQmdNxfXwPKdMLn76G5GrBrBvoR9DGg9gEfa\nPoKrs2u5HkNERERERHIp0NUE2ZmwtC/E/wF9lkOj8wvsfuO7v5izZh8jGyTScfoUXBo3JmTuHFwb\nNSrmDU+ewzpYuGshr255ldq1ajPzupl0bty53N5fREREREQKU6Cr7qyFT4bDvh/hthnQ9MoCuxes\n288rX//BY15xXPXuNNyaNCFkzmxc6tUrtxbi0+J55qdnWHNgDVcGX8n4LuOp416n3N5fRERERESK\npkBX3f0wCbYvhiufhra9C+z6eFsMY1bs5CH2c8PSGdQ67zxCZr2Dc+3a5Xb4VdGreHbNs6RkpfBM\np2e4u8XdGnwiIiIiIlJJFOiqs62L4MdJ0PZeuGJEgV3f7z7EE8u2Mzh1Nzd/PQePtm0JfnsGzj4+\nxbzZycnIyeDVza+y8LeFNK/TnNnXz6ZZnfKflCkiIiIiIsVToKuu9nwPnzwKTa+Cm6cVWGtu4/6j\nPLhoM/2ObuWOHxfh2akTwW+9iZOnZ/kc+vgeRqwawR/H/uCe8+7h8fDHqeVcq1zeW0REREREyk6B\nrjqK2wnL7oP6LeDu+XDCFMldBxIZOG8j90Svo8e69/G64nKCpk3Dyd39tA9rreX9P95nysYpeLp4\n8uY1b3J50OWn/b4iIiIiInJqFOiqm8QDucsTuHnBve+Du2/+rv3xKdw3ZwN3//EtPbZ8gs911xH4\nyssYN7fTPuzx9OOMXTuW76K+o0vjLky4ZAINPBuc9vuKiIiIiMipU6CrTjKSYNHdkJ4IAz8Hv3/W\nkItNSKfPrJ+5Y9un3P7rl/jedBONJ03EuJz+P+L1B9fz9OqnOZpxlCfDn6Tv+X1xMk6n/b4iIiIi\nInJ6FOiqi5wsWNYPDu2Ce5eBf5v8XcdSMuk762duWfsBN//xA7Xv6oH/uHEYZ+fTOmSWI4s3t77J\nnF/nEOobyhvXvEHLei1P90xERERERKScKNBVB9bCysdhz7dwy+vQ7Nr8XSkZ2Qycu55u3y6g2961\n1OnTh0ZPP4VxOr0raJGJkYxcNZJfj/zKnc3vZESHEXi6ls9QFRERERERKR8KdNXB6ldgy3y4/L/Q\n/r78zRnZOTzw7kauXjGTayM3Ue/+wTR4/PHTWgfOWsuKPSt4cf2LuDi5MPXKqVwXel15nIWIiIiI\niJQzBboz3S/L4Lvn4YKecNXo/M05DsvjizZxydLXuCJmG/UfGUb9hx46rTCXmJnIhHUT+Hz/54Q3\nCmfiZRPx9/Ivj7MQEREREZEKoEB3Jtu3Gv73EIRdBre8kb/WnLWWZ5Zt4aK5L9E5dicN//tf6g0a\neFqH2nZoGyNXjSQuNY5H2j3CoNaDcHY6vWfwRERERESkYinQnakO7Yal90K9c6DnQnD5Z+mBl1Zs\np9Vb47no0B80evYZ6t577ykfJtuRzTs73mHG9hkEeAXwbrd3ubDBheVxBiIiIiIiUsEU6M5ESXG5\na825uOeuNedRO3/XzC92EDplNG2O7sN/wvPU6dHjlA9zIPkAT61+ii2HtnBT05sY3Wk03m7e5XEG\nIiIiIiJSCRTozjQZybD4Lkg9AgNWQu2Q/F3Lvt9F/ef+S4vjUTSeMoXaN990yof5Yv8XjF87HgcO\nXrz0RW4+5+by6F5ERERERCqRAt2ZJCcbPhgIsTug91Jo3C5/1xdrduMx6lGaJMXS+NX/o/YN15/S\nIVKzUpm4YSL/++t/XFD/AiZdPolgn+DyOgMREREREalECnRnCmvh8xHw55fQfSqc+09g+2nDbvjP\ng4SmHCHgjTepc/UVp3SInfE7Gbl6JJGJkQy5YAgPXPgArk6u5XUGIiIiIiJSyRTozhRrX4NNs+GS\nx6DDoPzNWzftJuOhIfinJ+I/fTr1Lr/kpN/aYR3M2zmP17e8Tj2Pesy+YTYd/DuUZ/ciIiIiIlIF\nFOjOBL8uh6/HQOs74Zqx+Zt3b9lN4tBB1MlMpcGMGTS89OKTfutDqYd4+qenWX9wPdeFXsfYzmPx\nq+VXnt2LiIiIiEgVUaCrahHr4KMHIKQz3PoWODnlbt6ykyODB+OZnUndt2cR2OWik37r7yO/Z8za\nMWTkZDCu8zjuaH7HaS08LiIiIiIiZxYFuqoU/ye81xtqh0KvxeDqDsCBrTuIHTgIZyy+M2YR1qVd\nKW9UUHp2Oi9vepmlvy+lZd2WTLp8Ek39mlbEGYiIiIiISBVSoKsqyYdh4Z1gnHPXmvOsC8DhTds4\nMGgwWU4ueL8xgxZd2p7U2/5+9HdGrhrJnoQ99Du/H4+2fxQ3Z7fSXygiIiIiItWOAl1VyEyFJT0h\n+RD0Xwl1mwBwdMMmogcPIdnFA7dp02l7yQVlfktrLYt3L2bqpqn4uPnw9rVv0yWwS0WdgYiIiIiI\nnAGcylJkjOlqjPndGPOXMWZUEfsvN8ZsMcZkG2N6/GtfjjFmW96vFeXVeLXlyIEP74eYLdBjNgTl\nPht3fM1aogYN5oirF5mvvEWXy8oe5o6kHeHhbx9m0oZJXNz4YpbfslxhTkRERETkLFDqFTpjjDPw\nJnAdEA1sNMassNbuOqEsEugPPFnEW6RZa0/uvsGa7MunYfen0G0KnNcdgMQffiRq2CMc8KhLyguv\n0uOqC8v8dmti1jD6p9EkZSbxVMen6H1ebw0+ERERERE5S5TllsuOwF/W2r0Axpj3gFuB/EBnrd2f\nt89RAT3WHOvegvUzoPMw6DQUgMSvvibyP/9hv7c/h8ZMYdD1Zcu+mTmZTNsyjfm75tOsdjNmXj+T\nc+ucW5Hdi4iIiIjIGaYst1wGAlEnfB+dt62s3I0xm4wxPxtjbiuuyBgzJK9u0+HDh0/i7auJXR/n\nXp1reQtc9zwACZ+uJOqxx/jDN4jfR05mUPf2ZXqrvQl7ufeze5m/az69WvRiSfclCnMiIiIiImeh\nyhiKEmqtjTHGNAW+M8bssNbu+XeRtXYmMBMgPDzcVkJflSdqA3w4BII6wB0zwcmJ48uXc+CZZ/m1\nbhN+HTaG8beXHuastXz454dM3jiZWs61eO2q17gq5KpKOAERERERETkTlSXQxQDBJ3wflLetTKy1\nMXm/7zXG/AC0AwoFuhrryB5Y0gt8G0PvJeDqwdFFi4h7fgJbGp7LxoGjmNqzY6nPvSVkJPDcuuf4\nOuJrOgV04sVLX6ShZ8NKOgkRERERETkTlSXQbQSaG2OakBvkegH3lOXNjTF1gFRrbYYxpj5wCTDl\nVJutdlKOwKIeYC3c+wF41efI7Nkceull1gW0YlWvx3i7T0ecnUoOcxtjN/LU6qc4knaExy96nH6t\n+uFkyjSgVEREREREarBSA521NtsYMwz4EnAG5lhrdxpjxgObrLUrjDEdgI+AOsDNxpjnrLWtgJbA\n23nDUpyASf+ajllzZaXBe70hIQb6fYKt25T4N94k/o03WB3Uli9vfYD5/S+mlotz8W/hyGL6tunM\n2jGLEN8QFt64kFb1W1XiSYiIiIiIyJmsTM/QWWs/Az7717YxJ3y9kdxbMf/9urVAm9PssfpxOOCj\nobnPzt39Lja4I4dfeYUjs2bzXVhHPrluAO8N6oxXreL/+KOSohi1ahS/xP/C7c1uZ1THUXi6elbi\nSYiIiIiIyJmuMoainH2+fjZ3quX1L2DPu5m4CS9wbNEivm5+CR9c2ov377+Y2p5uxb78kz2f8ML6\nF3DCiZeueImuYV0rsXkREREREakuFOjK24Z3YN0b0HEotuMDxI4dy/H3P+DL869mYbvb+GBQZxr5\nuhf50uTMZCasn8DKvStp37A9Ey+bSGPvxpV8AiIiIiIiUl0o0JWn3Z/B5yOgRXfstc9zYNRTJH76\nKZ+1u5F3z72OpYM7EVbfq8iXbj+8nZGrRhKbEsvDbR9mcJvBuDjpH4+IiIiIiBRPiaG8xGyGDwZC\nQFvsLdOJeeK/JH39NZ91up13Qi5j4YCOtAzwLfSyHEcOs3bMYvr26fh7+TOv6zzaNmxbBScgIiIi\nIiLVjQJdeTi2Hxb3BO+GOO6YT/TjI0j5cRWfX9Gb6fXCmdXnIsLD6hZ6WWxKLKNWj2Jz3Ga6hXXj\n2c7P4uPmU/n9i4iIiIhItaRAd7pSj8LCHpCThaPXIqKeHEPq+vV81XUAr3u0YlrPtlzZovAC4F/t\n/4px68aR48jhhUtf4OamN5e6uLiIiIiIiMiJFOhOR3YGLO0DxyPIuXMJUSMnkbZtG9/d/iD/52jK\nhFtbc8uFBYeapGalMmXjFJb/uZzW9Voz+fLJhPiGVNEJiIiIiIhIdaZAd6ocDvjfgxCxhpzrXydy\nzNuk797Nmnv+w0vJATx5/bn0uTi0wEt2HdnFyFUjiUiMYHCbwTzU9iFcnVyr6ARERERERKS6U6A7\nVd+Nh1+Xk91xJJGTPyRz3z62DHqKCXF+DL60CQ9f1Sy/1GEdLNi1gFe3vEpd97rMun4WHQM6VmHz\nIiIiIiJSEyjQnYpNc+Gn/yOrWW8i31hD1oED7HpkLKP31KLHRUGM7t4y/3m4w6mHGf3TaNYdXMfV\nwVfzXJfnqO1eu4pPQEREREREagIFupP1x1ew8gkyG1xJ5Lt7yDlylIiRE3l8ew7Xn9+ISXe0yQ9z\nP0b9yLNrniUtO40xncfQo3kPDT4REREREZFyo0BXFr8sg2/HQ0I0AJkEEvF+Ao7UNOLGvsJD61Pp\n3LQer/Vuh4uzExk5Gbyy6RWW7F5CizotmHL5FJrWblrFJyEiIiIiIjWNAl1pflkGnzwKWWkAZCS4\nEPF9Nrgmkfjim9z/4zFaNfblnX7huLs68+exPxmxagR/Hf+LPi378NhFj1HLuVYVn4SIiIiIiNRE\nCnSl+XY8CX/CoV8akp3qDIBxsQR1T+XOnxIIquPBvAEd8XJz5r3d7/HyppfxcvXirWve4rKgy6q4\neRERERERqckU6EqRsD2egxv9sDlO/2y0lqyYY/ie58qCQZ0wzik8+t0Yfoj+gUsCL2HCJROo71G/\n6poWEREREZGzggJdKQ79WgebU3CbzXEi7pfaLHipIxGp2xj91WiOZxxnZIeR3NPyHpyMU9FvJiIi\nIiIiUo4U6EqRnVL0dptq+F/ETObunEtTv6ZMv3Y6Leq2qNzmRERERETkrKZLSaXIqtewyO1H/VyY\nu3Mud597N+/d9J7CnIiIiIiIVDoFulLMa9mNdGfnAtvSXWDhZS68etWrPNv5WTxcPKqoOxERERER\nOZsp0JXikybZvN3NicO+4AAO+8Lb3Zz4pv6NXBNyTVW3JyIiIiIiZzE9Q1cKj0ZfsSbQsqZNwT8q\nj+zVVdSRiIiIiIhILl2hK4V1OX5S20VERERERCqLAl0pArz8T2q7iIiIiIhIZVGgK8Xw9sNxd3Yv\nsM3d2Z3h7YdXUUciIiIiIiK59AxdKbo37Q7AtC3TiE2Jxd/Ln+Hth+dvFxERERERqSoKdGXQvWl3\nBTgRERERETnj6JZLERERERGRakqBTkREREREpJpSoBMREREREammFOhERERERESqKQU6ERERERGR\nakqBTkREREREpJoy1tqq7qEQY8xhIKKq+yhCfSC+qpuQGkufL6lI+nxJRdLnSyqSPl9S0c7Uz1io\ntbZBaUVnZKA7UxljNllrw6u6D6mZ9PmSiqTPl1Qkfb6kIunzJRWtun/GdMuliIiIiIhINaVAJyIi\nIiIiUk0p0J2cmVXdgNRo+nxJRdLnSyqSPl9SkfT5kopWrT9jeoZORERERESkmtIVOhERERERkWpK\ngU5ERERERKSaUqArA2NMV2PM78aYv4wxo6q6H6lZjDFzjDGHjDG/VnUvUvMYY4KNMd8bY3YZY3Ya\nY4ZXdU9Scxhj3I0xG4wx2/M+X89VdU9S8xhjnI0xW40xn1Z1L1KzGGP2G2N2GGO2GWM2VXU/p0rP\n0JXCGOMM/AFcB0QDG4He1tpdVdqY1BjGmMuBZGC+tbZ1VfcjNYsxJgAIsNZuMcb4AJuB2/TfMCkP\nxhgDeFlrk40xrsBPwHBr7c9V3JrUIMaYx4FwwNdae1NV9yM1hzFmPxBurT0TFxUvM12hK11H4C9r\n7V5rbSbwHnBrFfckNYi1dhVwtKr7kJrJWnvQWrsl7+sk4DcgsGq7kprC5krO+9Y175f+pljKjTEm\nCOgOzKrqXkTOVAp0pQsTEFzPAAADmklEQVQEok74Phr9MCQi1ZAxJgxoB6yv2k6kJsm7HW4bcAj4\n2lqrz5eUp1eBEYCjqhuRGskCXxljNhtjhlR1M6dKgU5E5CxgjPEGlgOPWWsTq7ofqTmstTnW2rZA\nENDRGKNbx6VcGGNuAg5ZazdXdS9SY11qrW0PdAMeznsMptpRoCtdDBB8wvdBedtERKqFvGeblgOL\nrLUfVnU/UjNZa48D3wNdq7oXqTEuAW7Je87pPeBqY8zCqm1JahJrbUze74eAj8h91KraUaAr3Uag\nuTGmiTHGDegFrKjinkREyiRvaMVs4Ddr7dSq7kdqFmNMA2NM7byvPcgdILa7aruSmsJa+5S1Nsha\nG0buz1/fWWv7VHFbUkMYY7zyhoVhjPECrgeq5cRxBbpSWGuzgWHAl+QOE1hmrd1ZtV1JTWKMWQKs\nA1oYY6KNMYOquiepUS4B+pL7N9vb8n7dWNVNSY0RAHxvjPmF3L8A/dpaq9HyIlIdNAJ+MsZsBzYA\nK621X1RxT6dEyxaIiIiIiIhUU7pCJyIiIiIiUk0p0ImIiIiIiFRTCnQiIiIiIiLVlAKdiIiIiIhI\nNaVAJyIiIiIiUk0p0ImISI1ljMk5YbmGbcaYUeX43mHGmGq5ZpGIiNQcLlXdgIiISAVKs9a2reom\nREREKoqu0ImIyFnHGLPfGDPFGLPDGLPBGNMsb3uYMeY7Y8wvxphvjTEhedsbGWM+MsZsz/vVJe+t\nnI0x7xhjdhpjvjLGeFTZSYmIyFlJgU5ERGoyj3/dctnzhH0J1to2wBvAq3nbXgfetdZeACwCXsvb\n/hrwo7X2QqA9sDNve3PgTWttK+A4cGcFn4+IiEgBxlpb1T2IiIhUCGNMsrXWu4jt+4GrrbV7jTGu\nQKy1tp4xJh4IsNZm5W0/aK2tb4w5DARZazNOeI8w4GtrbfO870cCrtbaCRV/ZiIiIrl0hU5ERM5W\ntpivT0bGCV/noGfTRUSkkinQiYjI2arnCb+vy/t6LdAr7+t7gdV5X38LPAhgjHE2xvhVVpMiIiIl\n0d8kiohITeZhjNl2wvdfWGv/XrqgjjHmF3KvsvXO2/YIMNcY81/gMDAgb/twYKYxZhC5V+IeBA5W\nePciIiKl0DN0IiJy1sl7hi7cWhtf1b2IiIicDt1yKSIiIiIi8v/t2QEJAAAAgKD/r9sR6ItoyqED\nAACYcugAAACmBB0AAMCUoAMAAJgSdAAAAFOCDgAAYCoUbMnCDD1GnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb140d97c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "    print('running with ', update_rule)\n",
    "    model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "    solvers[update_rule] = solver\n",
    "    solver.train()\n",
    "    print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "    plt.subplot(3, 1, 1)\n",
    "    plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "\n",
    "    plt.subplot(3, 1, 2)\n",
    "    plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "    plt.subplot(3, 1, 3)\n",
    "    plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "\n",
    "for i in [1, 2, 3]:\n",
    "    plt.subplot(3, 1, i)\n",
    "    plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "best_model = FullyConnectedNet([100, 100, 100, 100, 100, 100], weight_scale=1e-5, \n",
    "                              use_batchnorm=True, dropout=0.8)\n",
    "\n",
    "solver = Solver(best_model, data,\n",
    "                num_epochs=20, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={'learning_rate': 1e-4},\n",
    "                verbose=False)\n",
    "solver.train()\n",
    "##########################\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.541\n",
      "Test set accuracy:  0.52\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
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